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JSAI (Journal Scientific and Applied Informatics)
Published by Universitas Muhammadiyah Bengkulu
ISSN : 26143062     EISSN : 26143054     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal terbitan dibawah fakultas teknik universitas muhammadiyah bengkulu. Pada jurnal ini akan membahas tema tentag Mobile, Animasi, Computer Vision, dan Networking yang merupakan jurnal berbasis science pada informatika, beserta penelitian yang berkaitan dengan implementasi metode dan atau algoritma.
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Articles 547 Documents
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Komparasi FTK Imager dan Autopsy dalam Investigasi Cyberbullying Telegram Menggunakan Framework DFRWS Arinaa Manaasika; Fahmi Fachri
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10419

Abstract

This study aims to investigate cyberbullying cases on the Telegram application using the Digital Forensic Research Workshop (DFRWS) framework by comparing the performance of FTK Imager and Autopsy forensic tools. The investigation process consisted of identification, preservation, collection, examination, analysis, and presentation stages to obtain digital evidence from Android devices. The results showed that FTK Imager successfully recovered text chats, voice notes, and metadata with a 100% success rate for text-based artifacts, but failed to recover multimedia files such as photos and videos. Meanwhile, Autopsy successfully recovered chats, photos, videos, metadata, and deleted files with a 100% success rate for multimedia artifacts and deleted file recovery. Overall, FTK Imager achieved a digital evidence recovery rate of 58.3%, while Autopsy achieved 83.3%. Digital evidence integrity validation using MD5 and SHA1 hashing produced identical hash values before and after the investigation process, indicating that the integrity and authenticity of the evidence were successfully maintained. The findings demonstrate that the combined use of FTK Imager and Autopsy provides a more effective, systematic, and comprehensive digital forensic investigation process for handling cyberbullying cases on Telegram.
Optimalisasi Pencarian Coffeeshop Terdekat Menggunakan Algoritma Haversine pada Sistem Mobile Recommendation Muhammad Abiyaca Alma'aarij; Sulistyo Dwi Sancoko
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10479

Abstract

This study aims to optimize the search for nearby coffee shops using the Haversine algorithm in a mobile recommendation system based on Location-Based Filtering (LBF). The system was developed by utilizing GPS, OpenStreetMap, and Firebase to provide real-time coffee shop recommendations according to the user’s location. The research methodology consisted of problem identification, coffee shop location data collection, implementation of the Haversine algorithm for geographic distance calculation, application of the Location-Based Filtering method to sort recommendations based on the nearest distance, and system evaluation using User Acceptance Testing (UAT) and distance accuracy comparison with Google Maps. The results showed that the system was able to calculate location distances with an average accuracy rate of 98.86% compared to Google Maps, with a distance difference ranging only from 0.03 to 0.05 km. In addition, the system successfully provided fast and relevant coffee shop recommendations based on the user’s real-time location. These findings indicate that the combination of the Haversine algorithm and Location-Based Filtering method is effective for implementation in a mobile-based coffee shop recommendation system to improve location search efficiency in real time.
Integrasi Role-Based Access Control dan First In First Out pada Sistem Point of Sales Berbasis Android untuk Optimalisasi Pengelolaan Transaksi dan Inventori UMKM Bengkel Lilo Puji Pratama
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10501

Abstract

This study aims to develop an Android-based Point of Sales (POS) system to improve transaction and inventory management in automotive repair shop SMEs. The main problem identified was that transaction recording and inventory management were still performed manually, resulting in delays and inventory data inconsistencies. The research employed the Waterfall method, which includes requirement analysis, system design, implementation, testing, and system evaluation stages. The system was developed using Kotlin and Firebase Realtime Database by implementing Role-Based Access Control (RBAC) for user access security and the First In First Out (FIFO) method for inventory management. System evaluation was conducted using black-box testing, System Usability Scale (SUS), operational efficiency analysis, and security testing. The results showed that all system features functioned properly. Usability evaluation involving 40 respondents through Google Forms obtained an SUS score of 82.5%, categorized as “Excellent.” The system also improved operational efficiency by reducing transaction recording time by 80% and increasing item search speed by 83% compared to the manual method. The implementation of RBAC and FIFO successfully improved access security and inventory management consistency in automotive repair shop SMEs.
Sistem Rekomendasi Bidang Studi Perguruan Tinggi bagi Siswa SMA Menggunakan Metode Hybrid Random Forest dan K-Nearest Neighbors Berbasis Profil Alumni Dzaky Abdur Razaq; Joko Aryanto
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10529

Abstract

Choosing a higher education field of study remains a challenge for senior high school students because decisions are often influenced by personal assumptions, social environment, and study trends without measurable academic and interest-based mapping. This study developed a field-of-study recommendation model based on alumni profiles using a Hybrid Random Forest and K-Nearest Neighbors approach. The dataset consisted of 2,440 records, 16 variables, and eight field-of-study categories. The research stages included data collection, data curation, preprocessing, 80:20 data splitting, Random Forest and K-Nearest Neighbors model training, probability fusion with weights of 0.6 and 0.4, and evaluation using confusion matrix, accuracy, precision, recall, and F1-score. The results showed that the hybrid model achieved the best performance with an accuracy of 98.77%, precision of 0.99, recall of 0.99, and F1-score of 0.99. This result was higher than Random Forest with an accuracy of 98.36% and K-Nearest Neighbors with an accuracy of 96.31%. Feature importance analysis indicated that interest-related variables contributed the most to the recommendation process. These findings show that the hybrid model can be used as a basis for developing a web-based field-of-study recommendation system.
Komparasi Algoritma Klasifikasi Performa Akademik Mahasiswa Bisnis Digital: SVM, Random Forest, XGBoost, dan LightGBM dengan Penanganan Class Imbalance Menggunakan SMOTE Lili Indah Sari; Burham Isnanto; Wishnu Aribowo Probonegoro
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10535

Abstract

This study aims to compare the performance of classification algorithms, namely Support Vector Machine (SVM), Random Forest, XGBoost, and LightGBM, in predicting the academic performance of Digital Business students at ISB Atma Luhur by handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The dataset consisted of 326 student records with 55 questionnaire-based Likert-scale features, GPA, and semester data classified into two academic performance classes. The research stages included data preprocessing, normalization, SMOTE implementation, feature selection using feature importance, model training, and evaluation using accuracy, precision, recall, F1-score, F1 Macro, AUC-ROC, and training time metrics. The results showed that the XGBoost algorithm achieved the best performance with an accuracy of 0.8621, an F1 Macro score of 0.85, and an AUC value of 0.91. LightGBM produced performance close to XGBoost while providing faster training time. The implementation of SMOTE successfully improved minority class classification performance across all algorithms, particularly in terms of F1-score. The findings indicate that the combination of boosting algorithms and class imbalance handling techniques is effective for machine learning-based academic performance prediction systems.
Pengembangan Sistem Media Intelligence ESG Berbasis NLP Bahasa Indonesia Menggunakan TF-IDF dan IndoBERT Lukman Hakim Moeslich; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10586

Abstract

Monitoring Environmental, Social, and Governance (ESG) issues in Indonesia’s nickel mining industry has become increasingly important due to growing demands for transparency and sustainability. However, automated ESG media analysis for Indonesian-language news remains limited. This study aims to develop an ESG media intelligence system based on Natural Language Processing (NLP) to analyze media perception toward PT Indonesia Weda Bay Industrial Park (IWIP) and PT Weda Bay Nickel (WBN). The proposed system employs an eight-stage pipeline consisting of automated news collection, Indonesian text preprocessing, ontology-based ESG labeling, text classification using TF-IDF + LinearSVC and IndoBERT, as well as sentiment and ESG risk trend analysis. A total of 1,693 news articles published between January 2020 and May 2026 were collected, with 1,320 articles successfully labeled using an ontology-based weak supervision approach. Experimental results show that the best TF-IDF configuration achieved a Macro-F1 score of 0.7693, while IndoBERT achieved 0.7698. The findings indicate that TF-IDF remains competitive with transformer-based models on limited Indonesian ESG datasets. Media analysis revealed that IWIP received predominantly negative media perception on environmental and social issues, while WBN showed relatively more positive governance-related coverage. This research contributes to the development of Indonesian-language ESG media intelligence for the mining industry.
Implementasi E-Commerce Brand Fashion VASignature Berbasis Laravel dan Midtrans dengan Evaluasi Usability Menggunakan USE Questionnaire imam
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10647

Abstract

The rapid development of information technology has encouraged businesses to transform conventional sales processes into digital-based systems to improve operational efficiency and service quality. VASignature, a local fashion brand, faces challenges in managing product data, sales transactions, and reporting processes due to the use of manual procedures. This study aims to implement an e-commerce system based on the Laravel framework integrated with the Midtrans Payment Gateway to support a more structured and automated sales process. The system was developed using the Software Development Life Cycle (SDLC) Waterfall model, which consists of requirements analysis, system design, implementation, testing, and evaluation stages. System testing was conducted using Black-box Testing to evaluate functional performance and the USE Questionnaire to assess usability based on four dimensions: Usefulness, Ease of Use, Ease of Learning, and Satisfaction. The results indicate that the developed system was successfully implemented, with all Black-box Testing scenarios achieving a 100% success rate. The usability evaluation produced scores of 88.40% for Usefulness, 90.13% for Ease of Use, 91.20% for Ease of Learning, and 89.47% for Satisfaction, resulting in an overall average score of 89.80%, which falls into the excellent category. These findings demonstrate that the proposed system not only functions effectively from a technical perspective but also provides a positive user experience and high user acceptance. Therefore, the developed e-commerce system is considered suitable for supporting digital sales operations and business management at VASignature.

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