Jurnal Infra merupakan jurnal nasional yang diterbitkan dua kali dalam setahun (Februari dan Agustus) oleh Program Studi Informatika, Universitas Kristen Petra, Surabaya sejak tahun 2013. Jurnal Infra memuat artikel penelitian di bidang Teknologi Informasi yang berfokus pada Artificial Intelligence, Game Development, Cyber Security, Mobile Application Development, Enterprise Information System, dan Business Intelligence.
Sales Time Series Data Analysis Using a Combination of Supervised andUnsupervised Learning for Business Strategy Optimization at PT X
PT X is an industrial gas company with an extensive operational network, yet it faces challenges in analyzing sales performance and price inconsistencies across regions. The lack of a data-driven analytical system makes it difficult for the company to identify sales trends, product performance, and branch contributions objectively. This study aims to develop a data-driven analytical system to support business decision-making. Sales trend analysis was conducted using SARIMAX, compared with SARIMA and XGBoost, with SARIMAX showing the highest accuracy. Evaluation using MAPE and RMSE indicated error rates of 18% for SARIMA, 14% for XGBoost, and 5% for SARIMAX. Branch clustering was tested using K-Means and GMM, with GMM selected due to faster execution and better clustering quality (Silhouette Score 0.623 vs 0.622). Additionally, Linear Regression and Random Forest were applied for price standardization across branches, with Linear Regression providing more consistent predictions, high R², and relatively low errors. The results demonstrate an effective analytical system that supports more accurate and consistent business decision-making.
Prediksi Harga Sewa Tenant dengan Pendekatan Hybrid K-prototype Clustering dan Categorical Boosting pada Mall XYZ
Mall XYZ menghadapi tantangan dalam menetapkan harga sewa yang kompetitif dan berbasis data bagi tenant, terutama akibat ketimpangan performa tenant. Ketimpangan ini dipengaruhi oleh variabel seperti jenis usaha, lokasi, dan kapasitas finansial tenant. Untuk mengatasi tantangan tersebut, penelitian ini bertujuan membangun sistem pendukung keputusan yang mengintegrasikan segmentasi tenant dan prediksi harga sewa. Penelitian mengusulkan pendekatan hybrid yang menggabungkan K-Prototype dan K-Medoid Clustering untuk segmentasi tenant berdasarkan karakteristik multidimensional, serta algoritma Categorical Boosting (CatBoost) untuk memprediksi harga sewa. Dataset yang digunakan meliputi data historis kontrak sewa tenant periode 2015 hingga 2025, dilengkapi variabel eksternal berupa inflasi dan pertumbuhan PDRB Kota Surabaya untuk meningkatkan kontekstualitas prediksi. Pendekatan hybrid yang menggabungkan segmentasi tenant dan prediksi berbasis CatBoost menghasilkan peningkatan akurasi prediksi harga sewa. Segmentasi yang diperoleh memberikan wawasan berbasis data yang dapat digunakan oleh manajemen Mall XYZ dalam evaluasi kebijakan harga. Selain itu, sebuah dashboard juga dikembangkan untuk memvisualisasikan hasil prediksi dan analisis klaster guna mendukung proses pengambilan keputusan operasional.
Aspect-Controlled Summarization in Indonesian Language Scientific Papers Using Longformer Encoder-Decoder
In the current information era, the number of scientific papers published in Indonesian continues to increase, creating a need for automated document summarization systems that enable efficient and rapid access to essential information. This study investigates the application of aspect-controlled summarization to Indonesian scientific articles using the Longformer Encoder-Decoder (LED) model. The proposed system aims to generate summaries that explicitly consider predefined aspects, namely Purpose, Method, Findings, and Value. This study employs the Indonesian-translated version of the FacetSum dataset, which has undergone preprocessing and structural reorganization to support aspect-based summarization. The LED model is fine-tuned using this dataset and evaluated at both the whole-document level and the facet level. Model performance is assessed using ROUGE metrics as the primary quantitative evaluation method, complemented by qualitative analysis. Experimental results indicate that while the LED model is capable of generating aspect-based summaries, its performance remains limited when compared to baseline models trained on English datasets. The obtained facet-level ROUGE-L scores are 34.28 for Purpose, 19.45 for Method, 19.37 for Findings, and 21.37 for Value, whereas the BART-Facet model on the original English FacetSum dataset achieves scores of 42.55, 28.07, 28.98, and 28.70, respectively. Further analysis suggests that this performance gap is primarily attributable to architectural limitations of the LED model for aspect-based summarization tasks, as the ability to process long input sequences does not inherently translate into improved facet-level summary quality.
SISTEM PENERJEMAH MULTI BAHASA TERINTEGRASI DENGAN GEMINI UNTUK MENINGKATKAN AKSESIBILITAS KONTEN TEKS DI GMS CHURCH
Dalam lingkungan gereja multibahasa seperti GMS Church, perbedaan linguistik sering menjadi hambatan dalam penyampaian pesan rohani dan informasi pelayanan. Tanpa sistem penerjemahan yang memadai, sebagian jemaat berisiko mengalami keterbatasan pemahaman sehingga mengurangi keterlibatan dalam kegiatan gereja. Penelitian ini mengembangkan sistem penerjemah berbasis web dengan integrasi kecerdasan buatan Gemini. Sistem dilengkapi dengan fitur penerjemahan otomatis, validasi berjenjang dan manajemen proyek untuk memastikan hasil terjemahan yang akurat, konsisten, dan sesuai konteks keagamaan. Hasil pengujian melalui User Acceptance Test (UAT) menunjukkan bahwa sistem mampu meningkatkan efisiensi dan kualitas penerjemahan, serta membantu jemaat dari berbagai latar belakang bahasa untuk mengakses materi gereja dengan lebih mudah
Implementation of the Gamification Method for Employee Training Systems at PT XYZ Using the Moodle Learning Management System
This study aims to develop a Moodle-based Learning Management System (LMS) to address employee training challenges at PT XYZ. The main obstacle faced by the company is the onboarding process, which was previously conducted manually via WhatsApp, resulting in unstructured materials and difficult monitoring. Therefore, this system is designed to provide modular learning paths, automated evaluation mechanisms, and data-based progress tracking features to improve training management. As a primary solution to enhance user engagement, this study applies gamification methods that include points (XP), levels, activity badges, and leaderboards. Its technical implementation is supported by the development of an additional plugin named "My Command Center," which functions to manage automatic badge assignment based on learning activity completion. This approach aims to transform the monotonous training process into a more interactive and motivating learning experience. Testing results indicate that the system operates stably without major errors and is able to significantly accelerate the onboarding process duration. Evaluation using User Acceptance Testing (UAT) proves that gamification integration effectively increases employee motivation, comprehension, and engagement, as evidenced by improved module completion rates, XP accumulation, and assessment results. Thus, the application of gamification within the LMS is proven to be an effective solution for enhancing training quality and efficiency at PT XYZ.
Evaluasi Akurasi Analisis Forensik Velociraptor dan Tools Tradisional pada Serangan LockBit dengan Pendekatan DFRWS
Peningkatan kasus ransomware seperti LockBit 3.0 mendorong kebutuhan akan analisis forensik yang cepat dan akurat. Selama ini banyak investigasi mengandalkan tools tradisional yang bekerja secara terpisah, sehingga proses korelasi artefak sering memerlukan waktu lebih lama. Velociraptor sebagai tools modular menawarkan pendekatan yang otomatis dan terpusat. Penelitian ini dilakukan untuk menilai apakah Velociraptor dapat memberikan tingkat akurasi yang setara atau lebih baik dibandingkan tools tradisional ketika digunakan untuk menganalisis serangan ransomware pada lingkungan yang disimulasikan. Hasil penelitian menunjukkan bahwa meskipun Velociraptor lebih cepat secara signifikan dalam akuisisi data, tetapi tools tradisional menghasilkan cakupan artefak yang lebih lengkap. Pada pengujian tiga alur serangan LockBit 3.0, Velociraptor tidak berhasil mendeteksi beberapa taktik MITRE ATT&CK yang sebenarnya dapat diidentifikasi oleh tools tradisional, yaitu pada teknik yang berhubungan dengan taktik Persistence, Privilege Escalation, Defense Evasion, Lateral Movement, Command and Control, serta Exfiltration. Temuan ini menunjukkan bahwa adanya trade-off antara penggunaan tools tradisional dengan Velociraptor.
Web Manajemen Rekam Medis dan Asesmen Psikologis Berbasis Web untuk Psikiater dengan Fitur Deteksi Interaksi Obat
Praktik psikiatri di Indonesia menghadapi tantangan kritis dalam pengelolaan rekam medis manual, efisiensi asesmen psikologis, dan keamanan resep polifarmasi. Penelitian ini mengembangkan sistem terintegrasi berbasis web yang menggabungkan Electronic Medical Record (EMR), asesmen psikologis digital terstandar (HAM-A dan HAM-D), dan deteksi interaksi obat menggunakan database lokal dan OpenFDA API. Sistem dibangun menggunakan Flutter dengan arsitektur Model-View-Controller (MVC) dan database MySQL. Evaluasi melalui User Acceptance Testing (UAT) menunjukkan peningkatan efisiensi signifikan: waktu registrasi pasien 96,7% lebih cepat (15 menit → 30 detik), pencarian rekam medis 98,4% lebih efisien (5-10 menit → 4,7 detik), dan waktu asesmen 75% lebih cepat (8-10 menit → 2m5 menit). Sistem mencapai akurasi perhitungan asesmen 100% (57/57 kasus) dan deteksi interaksi obat 90,4% (141/156 interaksi). Skor System Usability Scale (SUS) mencapai 95 (Grade A+), memvalidasi kelayakan sistem sebagai alat bantu klinis yang efektif untuk meningkatkan kualitas layanan kesehatan mental di Indonesia.
Analisis Penjualan dan Pelanggan Toko Swalayan di Kediri Menggunakan Market Basket Analysis dan Machine Learning
Inventory management and sales analysis in convenience stores are often handled manually, making it difficult for store owners to understand customer purchasing patterns and make effective operational decisions. This study analyzes transaction and inventory data from a convenience store in Kediri by combining Market Basket Analysis and machine learning to produce more measurable, actionable insights. The methods include FP-Growth to discover product associations, Decision Tree to predict inventory status (restock/overstock), K-Means to segment products, and Random Forest Regression to forecast monthly profit. The results show that consistent purchasing patterns can support bundling and product arrangement recommendations, while the classification, clustering, and regression models help improve stock monitoring, wholesale strategy, and financial planning. All outputs are implemented in an interactive dashboard to support practical use by the store owner.
Sistem Rekomendasi Investasi Saham Perusahaan di Bawah Naungan Danantara Menggunakan Long Short-Term Memory
Investasi memiliki peran penting dalam menjaga nilai kekayaan dan mengatasi dampak inflasi. Namun, banyak individu masih enggan berinvestasi karena ketakutan terhadap risiko, pengaruh emosi, dan bias kognitif. Kondisi ini semakin terlihat pada kasus peluncuran Danantara, di mana persepsi negatif masyarakat menurunkan minat investasi pada perusahaan-perusahaan di bawah naungannya. Oleh karena itu, diperlukan sebuah sistem rekomendasi investasi berbasis data yang mampu memberikan saran yang objektif untuk saham perusahaan-perusahaan tersebut. Penelitian ini mengembangkan sistem rekomendasi investasi saham berbasis prediksi harga menggunakan model Long Short-Term Memory (LSTM), dengan ARIMA dan Recurrent Neural Network (RNN) sebagai model pembanding. Model-model tersebut diterapkan pada data harga saham ANTM, BBNI, BBRI, BMRI, dan TLKM. Hasil prediksi dari masing-masing model diolah untuk menghasilkan rekomendasi buy, hold, atau sell sehingga dapat membantu pengguna dalam mengambil keputusan investasi. Hasil penelitian menunjukkan bahwa model LSTM memiliki akurasi prediksi yang baik dengan nilai MAPE antara 1.7%–2% pada lima saham yang diuji. Dari simulasi transaksi, LSTM menghasilkan rata-rata keuntungan tertinggi dibandingkan RNN dan ARIMA. Selain itu, LSTM juga memberikan akurasi rekomendasi terbaik terhadap pendapat ahli pada seluruh saham uji. Dengan demikian, LSTM menjadi model yang terbaik untuk sistem rekomendasi investasi yang dikembangkan.
PENETRATION TESTING WEBSITE UNIVERSITAS KRISTEN PETRA TERHADAP SERANGAN GSOCKET
Website Universitas Kristen Petra (UKP) merupakan platform penting yang menunjang berbagai aktivitas akademik dan administrasi. Namun, website ini telah menjadi target serangan injeksi GSocket, yang memungkinkan penyerang mendapatkan akses remote shell dan memodifikasi situs web untuk kepentingan ilegal, seperti promosi perjudian online (JUDOL). Serangan ini dapat mengancam keamanan data, merusak reputasi universitas, dan memberikan akses tidak sah ke sistem internal. Penelitian ini bertujuan untuk mengidentifikasi teknik injeksi GSocket dan celah keamanan yang dimanfaatkan oleh penyerang dalam mengakses dan memodifikasi fungsi aplikasi web UKP. Selain itu, penelitian ini menganalisis dampak serangan tersebut serta memberikan rekomendasi kepada tim Pusat Pengembangan Sistem Informasi (PPSI) UKP dalam menangani, mengidentifikasi, dan memitigasi ancaman GSocket. Metode yang digunakan dalam penelitian ini adalah White Box dan Black Box Penetration Testing, yang menggabungkan elemen pengujian dari perspektif pengguna dengan akses terbatas terhadap sistem. Hasil penelitian ini berupa laporan kerentanan yang ditemukan serta saran mitigasi yang dapat diterapkan untuk meningkatkan keamanan website UKP. Diharapkan penelitian ini dapat membantu meningkatkan kesadaran akan ancaman injeksi GSocket serta memberikan langkah-langkah pencegahan yang efektif guna melindungi integritas website dan data pengguna UKP.
Implementation of Multi-Agent with a Finite State Machine Approach in the Attendance Issue Reporting System at PT. XYZ
Operational issues in digital attendance systems such as recognition errors, network disruptions, or forgotten credentials are still frequently reported through informal channels, resulting in unstructured records that are hard to trace and prone to duplication or fraud. This study proposes a conversational, report-driven attendance incident system that combines a multi-agent approach with a finite state machine (FSM) to keep dialogues structured, preserve conversational context, and ensure data completeness. The system performs automatic information extraction from user inputs using a large language model, and conducts evidence pre-validation via optical character recognition and multimodal classification prior to human verification. The research methods include organizational needs analysis, design of the data model and interaction flow, formulation of FSM rules to govern dialogue stages, and integration of conversational intelligence, information extraction, and evidence validation components. Evaluation follows User Acceptance Testing (UAT) grounded in realistic business workflows at PT. XYZ, focusing on three aspects: (1) time efficiency compared to the manual reporting process, (2) data consistency and completeness by comparing chatbot variants with and without FSM control, and (3) reduction of initial manual workload for administrators during verification. Experimental results show the system accelerates reporting by ≥90% relative to the manual method, yields higher completeness with the FSM-controlled chatbot (approximately 96% complete versus ~70% without FSM), and achieves an ≈70% reduction in early administrative intervention. Evidence validation using text recognition and multimodal classification attains ~90–95% accuracy for common cases, effectively serving as a pre-screen before human review. These findings indicate that a multi-agent approach with FSM control can improve the speed, traceability, and reliability of attendance incident reporting.
Human Resources Web Application Penetration Testing Using PTES and OWASP Top 10
Web-based Human Resources (HR) applications store highly sensitive employee and organizational data, making them attractive targets for cyberattacks. This study presents a penetration testing assessment of a multi-tenant HR web application owned by PT. XYZ. The evaluation was conducted using a gray-box approach based on the Penetration Testing Execution Standard (PTES) and guided by the OWASP Top 10 (2021) framework. The testing process covered information gathering, threat modeling, vulnerability analysis, controlled exploitation, and post-exploitation analysis. The results reveal several critical and medium-risk vulnerabilities, including Broken Access Control in the form of Insecure Direct Object Reference (IDOR), security misconfigurations, outdated server components, and the absence of rate-limiting mechanisms in authentication features. These weaknesses could potentially lead to sensitive data leakage and account takeover attacks. This paper also provides concrete mitigation recommendations to strengthen access control, configuration hardening, and the overall security posture of the HR web application.
Aplikasi Channel Management dan Point of Sales pada perusahaan retail PT. XYZ dengan menggunakan metode Cross-channel dan Market Basket Analysis
PT XYZ is a company engaged in the retail and distribution of baby equipment located in Surabaya. Generally, the company has many stores ranging from physical stores and online stores. The problem faced at this time is that there is no system that can connect the company's internal systems including the cashier system, warehouse system, sales calculation system with the existing system in online stores such as in this case the Shopee system. To answer these problems, PT XYZ requires a website-based channel management system so that it can be integrated both on mobile and desktop. It is hoped that by using this technology, it can help companies improve the performance and sales performance of stores both physical stores and online stores. An integrated Multichannel Management System and Point of Sales Application can record every sale, stock management practically and efficiently so that when a stock transaction occurs it will always be synchronized along with transaction data from between online stores and offline stores.
Penerapan Artificial Neural Network dan Rule Based Classifier untuk Mengklasifikasikan Pendonor Darah Potensial pada Sistem Broadcast Pendonor
One of UTD PMI Surabaya’s task is to provide safe and quality blood when blood is needed in an emergency. The availability of blood at UTD PMI Surabaya can be erratic, because it depends on the number of donors that fluctuates and the storage time of blood is not long. Therefore, UTD PMI Surabaya needs a system to invite potential donors to meet blood needs when needed in an emergency, by minimizing blood wasted. The classification model and the creation of a recommendation system will produce a list containing donors who have been sorted by priority. Testing was carried out by dividing the data according to the conditions of the data collection environment (before the pandemic, during the pandemic and a combination of before and during the COVID-19 pandemic). The highest MRR value was obtained from the ANN model made from a combined data of 90% classification results using RBC and fake data. The accuracy value obtained from the model is 91.13% for training and 91.83% for testing. The resulting MRR value is 8.07 x 10-4 .
Penerapan Linguistic Inquiry and Word Count dan Random Forest Dalam Klasifikasi Personality Berdasarkan Data Posting Twitter Sehingga Dapat Ditentukan Gaya Belajar yang Sesuai
Big Five Personality is a powerful personality model for understanding the relationship between personality and various academic behaviors. Students' personality is very important for learning and has the potential to determine their academic achievement and learning style. However, not all students have the same knowledge, personality, and learning styles where these criteria affect learning. To find out, we usually use online tests and it takes a long time. In this study, a system was created to determine personality and learning style automatically based on Twitter post data. The method used in this research is LIWC or Linguistic Inquiry and Word Count and Random Forest. Random Forest was chosen because this method can classify class imbalances where in classifying the Big Five personalities from text data, not all of the data have the same number of personalities (extraversion, agreeableness, openness, conscientiousness, and neuroticism). The data text that will be used is data text from social media, namely Twitter with a total data of 9546 data. The results of Random Forest accuracy for balanced and imbalanced datasets are not very significant, such as the imbalanced CON personality has an accuracy of 0.499 while the balanced CON has an accuracy of 0.502 or also the imbalanced NEU personality has an accuracy of 0.502 while the balanced NEU has an accuracy of 0.519. While the results of learning style can be determined from the Big Five Personalities with an average Kendall Tau correlation value of 0.21. According to the compatibility survey of the respondents, respondents felt that the external web was more suitable with the average value of the respondent's suitability with the results of the external web of 4.5 for Big Five Personality and 4 for learning style results. Meanwhile, for the results of the program, the average obtained for the Big Five Personality is 3 while for the learning style it has an average value of 3.25
Sistem Registrasi Dan Identifikasi Wajah Untuk Akses Fasilitas Universitas Kristen Petra Dengan Kombinasi Facenet Dan Hierarchical Navigable Small Worlds
The Identity Cards of students are used to access the facilities and participate in events of Petra Christian University. The problem which arises from these cards is the misuse by the irresponsible group. For this, the university needs the face identification system as the alternative. This thesis is meant to build a fast, accurate, and easy to use face identification system. The methods used to solve the problem is a combination of Facenet and Hierarchical Navigable Small Worlds (HNSW). Facenet is used to process the face into a 128-dimension vector which will be used for searching. HNSW is a k Nearest Neighbor search method which is used in a large-scale search. Using the method, the system takes an average time of 1 second to identify faces.
Aplikasi Sistem Pengontrolan Turtle Tub Untuk Pemeliharaan Kura-Kura Red Belly Nelsoni Dengan Arduino
Having turtles, especially Red Belly Nelsoni turtle is very common, but usually the owner don’t really know how to properly taking care of their turtles, according to the parameters needed in turtle maintenance. The problem that the author wants to solve is by utilizing Blynk application and Internet of Things tools that has a function to control, monitor and maintain all parameters that needed in turtle maintenance, so that the owner of the turtle can more easily taking care of the turtle in the turtle tub. The test was carried out by giving 2 turtle tubs containing Red Belly Nelsoni turtles to 2 volunteers who carried out the turtle care and maintenance in different ways. From the result of the test carried out, the application has been able to help the volunteers in taking care of the turtles according to the parameter aspects that needed in turtle maintenance.
Aplikasi Monitoring Pada Tanaman Aglaonema menggunakan IOT
Aglaonema plants are quite difficult to cultivate because the keeper must know what Aglaonema plants need in order to thrive and be beautiful. Aglaonema plants have high sensitivity to roots and stems, as well as leaves. Aglaonema plants require a soil moisture level that is not too moist and not too dry, a soil pH of 6-7, and a placement that is not exposed to direct sunlight. The solution is made by utilizing advanced technology which makes an application that can monitor and maintain the fertility of soil conditions on the Aglaonema plant on a mobile basis and use IOT as a sensor tool to retrieve data which will later be sent to the android application. The trial was carried out by using 2 aglaonema plants to be treated simultaneously. Plant 1 will be treated by volunteers who have just touched Aglaonema so that it provides care like other plants in general, plant 2 will be treated using a monitoring application, trials are also carried out by adjusting the data obtained from aglaonema experts. From the test results, it was found that the effectiveness of the application in monitoring Aglaonema plants has an accuracy rate of 100%.
Sistem Informasi dan Rekomendasi Kegiatan Kemahasiswaan Universitas Menggunakan Content-Based Filtering pada Web App RE*ACH sebagai Pusat Informasi Kegiatan Kemahasiswaan Universitas untuk Mahasiswa
In living the college life in Petra Christian University, each college students have his own problems. One of them is the problem of meeting the requirement for Student Activity Credit Unit (SKKK). There are also various reasons for each student to have this kind of problem. However, getting the opportunity to pass the selection as a committee member or participate in some student activities is one of the reason that the students complain most. Therefore, an information system and recommendation system for student activities must be created in a centralized platform that can be accessed by all Petra Christian University students. In helping the information system to be more precise in providing information, recommended system is added to the system so that the information related to the users can be addressed correctly to the users who really need it. In making the recommendation system, the ContentBased method with cosine similarity is used because the method tends to recommend products based to each user’s individual preferences. Users of RE*ACH application are all Petra Christian University students, so the dataset will consists of personal data from all Petra Christian University registered in the application.
Prototype Website Monitoring Parameter Energi Listrik pada Gedung A, B, C, D, & I di UK PETRA
In this modern era, technology is developing very quickly which can facilitate human needs. The fast developed technology need more energy resources such as electricity. This thesis is intended to display electrical energy parameter in the Petra Christian Building via website to show the energy consumption patterns. The data processing method is carried out using PHP on the website. Predictive data analysis using data from Petra Christian University Building which is sent via Gateway to WiFi/LAN. The result of the thesis shows that, the result of the data pattern can help provide an understanding of the pattern of electricity consumption in each building. The regression prediction result has a slope of about -0.75% from the original data. The survey result has an overall rating of 4,375 out of 5 which indicates that users are satisfied with this website.
Aplikasi Self Management untuk Mencatat Jadwal Kegiatan Dengan Speech to Text Menggunakan Google API Berbasis Android
Self Management is a person's ability to control himself against an action. But there are still many people who underestimate this, especially in terms of scheduling activities. People tend to only rely on memory and organize activities incorrectly. This makes people think a lot and tend to forget things. Thus, a scheduler app can be a perfect solution for busy people. This paper was made to create an application for scheduling using speech to text. The features of the application are calendars, to-do lists, notes, and speech to text. Speech to text using the Google Speech API. Calendar connects directly to Google Calendar using the Google Calendar API. Other features are stored in a local database in the form of SQLite. The application was tested by measuring the speed of making a schedule using a stopwatch. The result is that the schedule can be made in 10.426 seconds using speech to text. By using the Google Speech API in making the schedule, the accuracy obtained reaches 96.45%.
Sistem Pendukung Keputusan Pemberian Kredit berdasarkan Klasifikasi Kelancaran Pembayaran Kredit Menggunakan Metode VIKOR pada Bank XYZ
Banks must carry out complex assessments before being able to determine who is the most eligible prospective debtor who can be given a loan. This is due to limited funds and the risk of bad credit cases. The limited manpower and manual processes cause the whole process of lending at XYZ Bank to be prone to human error and become inefficient. As a solution for XYZ Bank to overcome existing problems, a credit decision support system is needed that can assist XYZ Bank in selecting and determining prospective debtors who can be given loans. Therefore, in this study, the KNearest Neighbor method was used to assist XYZ Bank in predicting the smoothness of credit payments of a prospective debtor. Then, this research continues with ranking using the VIKOR method to determine who is the most ideal debtor candidate to be given a loan. Based on the results of the classification test using both training data and new data, the highest accuracy is obtained at 100% for each type of loan. Based on the results of the ranking test, the accuracy of the business loans ranking is 83.33%, the accuracy of the consumer loans ranking is 80.33%, and the accuracy of the various-purpose loans ranking is 70%. The results of the questionnaire evaluation in system testing conducted by 6 respondents assessed that the application design was 76.67% good, the application functionality was 86.67% good, the ease of use of the application was 83.33% good, the application answered the needs was 86.67% good, and the overall application was 90% good.
Aplikasi Penerjemah Kegiatan Seminar Menjadi Video Bahasa Isyarat BISINDO Dengan Speech To Text
Information at this time is very much needed to increase our knowledge. However, this delivery can be hindered by several conditions such as the inability to hear the deaf community. Based on data from the Data and Information Center of the Ministry of Health of the Republic of Indonesia in 2019, 7.09% of the Indonesian population is deaf. In addition, the delivery of information at the seminar can be hindered by noise and participants sitting far from the speaker will have difficulty hearing the speaker's voice. In this study, we will use Speech-To-Text on the Android application which aims to help translate information in the form of voice delivered as at a seminar into text and will be converted into BISINDO sign language video. The results of testing the use of the Speech-To-Text feature in the application that has been made show that it is able to accommodate approximately 100 words in 1 minute at a time when the speaker speaks without any pause. The Speech-To-Text feature used takes approximately 2 seconds to translate the received voice and the time lag required by the speaker device to the participant's device takes approximately 3-5 seconds after using 5 different internet speeds. For the accuracy of the Speech-To-Text feature that was tested using 3 narrations read by 4 different people, the accuracy of the Speech-To-Text feature has an accuracy of above 80% in general, although there is an accuracy that is below 80% due to the ambiguity of the pronunciation.
Rancang Bangun Aplikasi Panggil Tukang Berbasis Android di Daerah Surabaya
Nowadays, many people are using android applications in their daily lives. Many kind of applications have been made to make everyday jobs and daily routines become simpler and easier. Looking for AC Service and home repairman is one of them. In Surabaya, looking for service providers is stil use with manual search which is asking to their neighboor. But the problem is, when the people just move in the new neighborhood, and just barely know anyone around, or urgently need to repair your air conditioner problem, and they just don't know whom to ask for help. Because of the problem, Writer make an application Panggil Tukang for helping people find good technicians especially people who lives in Surabaya. The features available on this application will be help the user to find the right technician. The user can get the interesting experience to find a good technicians in this application. Features such as price negotiation, payment methods, and chat will help the user to communicate with the service provider to find the best solution for them. This application simply make life much easier for people who are looking for the right technician's help. On the other hand, this application also provide help for all the technicians' service provider who are looking for people who needs their service. The application and website were built base on PHP language code and Kotlin. While the database we are using to make this application is Firebase.
Implementasi Text Summarization pada Review Aplikasi Super di Google Play Store Menggunakan Metode Maximum Marginal Relevance
Super App is an app for reseller agents who sell and distribute basic necessities in tier 2, 3 cities and rural Indonesia. The Super app has been downloaded by around 50,000 users on the Play Store. Various reviews or reviews have also been given by users who have downloaded the Super application. Whether we realize it or not, customer opinions / reviews given on Google play, a little or a lot, will have an influence on potential customers. Based on the problems that occur, this research will implement a text summarization program on Super App reviews with the implementation of the MMR and TF-IDF methods, so that from the large number of existing reviews, only a few important sentences can be extracted, so that the conclusion making process will become easier. The results of the research using the MMR method produced an average precision value of 40.4% in 3 trials, and with the highest precision value of 60.4% in the experiment using the parameter value = 0.7
Pewarnaan Otomatis Sketsa Gambar Menggunakan Metode Conditional GAN Untuk Mempercepat Proses Pewarnaan
Anime is a Japanese animation that consists of many frames of images. Images that used to make an anime can be made using hand-drawn or using digital-drawn. It takes a lot of time to make an anime. In making anime for 1 second, it needs a total of 24 frames, this is why it takes a lot of time to make anime and also takes a lot of money. Each image also needs to be colored, this is also why making anime takes so much time. The method used in this research is GAN (Generative Adversarial Network) or should we call C-GAN (Conditional Generative Adversarial Network) to make coloring anime sketches easier. Dataset that is used in this research is a pair of sketch images and sketch images that have already been colored.
Adaptive Sparse Transformer untuk Meningkatkan ROUGE-1 Score pada Text Summarization Scientific Paper
Technology advancement and internet causes lots of information that can be accessed at any time. Journal article is one of such many information that’s available that requires time to read thereof in need of automatic summary. Automatic Text Summarization (ATS) basically a process of making a new text that’s smaller than the original text without removing the meanings from the entire input text. The process of making automatic text summarization can be done in extractive and abstractive way. A summary that was made by an extractive method only able to generate a summary with a word that’s included in the original text, whereas summary that was made by an abstractive method can generate a summary that include word that does not exist in the original text. In the previous research in abstractive summarization is found is not optimal thereof need an improvement. The method used in this research is an abstractive summarization with Adaptive Sparse Transformer. Things that will be done in this research are scraping dataset arxiv machine learning, making the dataset, processing the data and trials on hyperparameter configuration in the model to see ROUGE-1 precision performance. The dataset used is Arxiv Scientific Paper dataset and Arxiv Scientific Paper+Machine Learning dataset. The results of this research showed that the method used capable to compete with state of the art methods with average R-1 precision score of 39.4 for Arxiv Scientific Paper+MachineLearning and 42.5 for Arxiv Scientific Paper.
Sistem Rekomendasi Pembelian Laptop dengan K-Nearest Neighbor (KNN)
Over a decade, the number of people who needs a laptop for their job is increased because of its slim design and internal battery that makes it easier to carry anywhere, if compare to an old-fashioned computer. However, the choices of laptop specifications are various, namely the laptop model, brand of the processor, the speed of processor, size of the screen, RAM, price, etc. Not all people knows the indicator of choosing the laptop that fit to their wants and needs. In this study, the process of recommendation system is used a methods namely K-Nearest Neighbor Collaborative Filtering. This method will estimate the distance with Euclidean formula between the users' criteria and the survey. The closest distance is considered as the recommendation. The data testing is done through counting the accuration based on the recommendation's result which is given to the user. The survey is used for counting the total of valid recommendation. he results of this study using a satisfaction survey that has been surveyed to 10 laptop shop employees. Based on the survey results, average accuracy of testing the recommendation results is 84%. Testing the appearance of the website is also quite good because most users give a value or rating of 4 or 5. Based on the testing results, it can be concluded that the appearance of the website and the results of recommendations with the K-Nearest Neighbor method is pretty good because they are in accordance with the criteria and needs of users.
Prediksi Kebutuhan Darah Menggunakan Metode ARIMA Dengan Mempertimbangkan Faktor Deterioration
Blood has various uses that are very important for the human body. However, blood can only be donated in limited quantities because it can only be produced by humans. This blood donation activity is organized by the Blood Transfusion Unit (UTD), one of which is UTD PMI Surabaya. UTD PMI Surabaya also produces and stores blood products and distributes them to hospitals or directly to patients who need blood. Due to the uncertain amount of blood demand, UTD PMI Surabaya needs to make predictions in order to meet the blood needs. But blood has an expiry that also needs to be considered. Meanwhile, the blood demand prediction system still uses human estimates. Therefore, an information system is needed that can assist in predicting the need for blood by considering the expiration date of the blood. The application made in this research is a web application that can assist in making predictions using the ARIMA method. Then a calculation will be carried out by considering the deterioration of the blood to determine the need for blood in the next month. Based on the test results, the application is able to predict blood needs. The ARIMA model used to predict WB blood components is ARIMA (7,0,6) with a RMSE of 58,91. While the ARIMA model used to predict the TC blood component is ARIMA (5,0,6) with a RMSE of 272,46.
Penerapan Metode Multiplicative Decomposition dan Autoregressive Integrated Moving Average dalam Prediksi Penjualan Produk Manufaktur pada PT. XYZ
PT. XYZ is a company engaged in the manufacture of drinking water (AMDK). PT. XYZ conducts product sales transactions in large quantities every day, so the large number of existing sales transactions will certainly affect the daily necessities inventory in the company. The problem that occurs in the company is regarding overstock and understock. Based on the problems that occur, the researchers will apply the Multiplicative Decomposition and Autoregressive Integrated Moving Average (ARIMA) methods to process a large number of sales-data which is used as information. So, the purpose of this study is to implement the Multiplicative Decomposition and Autoregressive Integrated Moving Average (ARIMA) methods to predict sales of mineral water goods at PT. XYZ. The test system will use the Mean Absolute Percentage Error (MAPE) method.