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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Polinema (JIP) Journal of Elektronik Sistem InformasI ALMUISY: Journal of Al Muslim Information System
Didiek Trisatya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Digitalisasi Pengembangan Sistem KKN Universitas Pancasakti Tegal Berbasis GIS Menggunakan Metode Prototyping Didiek Trisatya
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.6160

Abstract

Universitas Pancasakti Tegal is a private university in Central Java. Among them is the existence of an obstacle regarding the manual registration process for participants, not knowing the location points for administering KKN at universities for students, the absence of reporting of location points for the KKN village area for Lecturer Field Supervisors (LFS) and the KKN committee conducting the search the shortest way to find an important location, the location for administering the KKN, and filling out the KKN implementation schedule based on the location where it has not been digitalized so that the KKN implementation has not been managed effectively. Therefore, designing an Online KKN system is necessary to replace the manual system. By using the prototyping method, a prototyping system is produced that acts to connect developers with users to interact with each other in the continuity of information system development. The research results were obtained by applying API maps in the Online KKN system using Mapbox GL JS v2.11.0 to help access locations and applying markers as markers for a location as well as applying algorithms to solve the problem of finding the shortest route from the starting point to the endpoint using the Dijkstra algorithm.
Sistem Informasi Berbasis Web untuk Pendaftaran Kompetisi Internasional Didiek Trisatya; Priyo Haryoko; Gunawan Gunawan; Nur Tulus Ujianto
JESII: Journal of Elektronik Sistem InformasI Vol 3 No 2 (2025): Journal of Elektronik Sistem InformasI - JESII (DECEMBER)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v3i2.4409

Abstract

The management of international student competitions in higher education institutions often faces challenges related to fragmented registration processes, manual data handling, and inefficient submission management. These issues may lead to data inconsistency, administrative errors, and limited transparency for participants. This research aims to design and implement a web-based information system that integrates competition registration and work submission into a single platform. The study adopts a system development–oriented approach, including requirement analysis, system design, implementation, and functional testing. The proposed system supports an integrated registration and submission process for participants, role-based access for administrators, administrative verification, and real-time status monitoring. The implementation results indicate that the system operates according to functional requirements and improves administrative efficiency, data accuracy, and accessibility for users. By centralizing registration and submission processes, the system reduces redundancy and simplifies competition management workflows. This research provides a practical solution for managing international student competitions, with a case implementation at Universitas Pancasakti Tegal.
Financial Instrument Prediction Using Information Retrieval Algorithm Approach on the Comparison of Tf-IDF, Bm25, and Cosine Similarity Methods Purwa Hapsari, Diajeng Tyas; Diajeng Tyas; Gunawan; Raharjo, Gunawan; Trisatya, Didiek; Ujianto, Nur Tulus
ALMUISY: Journal of Al Muslim Information System Vol. 5 No. 1 (2026): ALMUISY: Journal of Al Muslim Information System
Publisher : STMIK Al Muslim

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Abstract

Penelitian ini bertujuan untuk mengembangkan sistem prediksi instrumen finansial yang inovatif menggunakan pendekatan algoritma Information Retrieval. Dalam era digitalisasi pasar keuangan yang semakin kompleks, investor membutuhkan alat bantu pengambilan keputusan yang akurat dan berbasis data. Tiga metode yang dibandingkan adalah Term Frequency-Inverse Document Frequency (TF-IDF), Best Matching 25 (BM25), dan Cosine Similarity. Data dikumpulkan dari Yahoo Finance mencakup 18 instrumen finansial dari berbagai kelas aset termasuk saham teknologi Amerika Serikat, saham blue chip Indonesia, Exchange Traded Funds, dan mata uang kripto. Metodologi penelitian merepresentasikan setiap instrumen sebagai dokumen tekstual berdasarkan indikator teknikal. Hasil penelitian menunjukkan ketiga metode memiliki korelasi peringkat tinggi dengan Spearman Correlation di atas 0.94. Metode BM25 memberikan performa terbaik dengan NDCG sebesar 0.8756 dan MAP sebesar 0.8124. Instrumen yang diprediksi mengalami kenaikan meliputi NVIDIA, AMD, Bitcoin, Amazon, dan Microsoft.
Improving Customer Churn Detection Through Balanced Ensemble Learning Didiek Trisatya; Priyo Haryoko
Jurnal Informatika Polinema Vol. 12 No. 3 (2026): Vol. 12 No. 3 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i3.9500

Abstract

Predicting customer churn represents a major challenge for telecommunication providers, driven by fierce market competition and frequent customer switching that can significantly threaten long-term revenue stability. Failure to accurately identify customers with high churn potential often leads to ineffective retention strategies. This study examines the effectiveness of integrating data balancing techniques with ensemble learning models to enhance churn prediction performance on imbalanced datasets. A quantitative experimental method is applied using a publicly available telecommunications dataset. The preprocessing phase focuses on handling incomplete records, transforming categorical attributes into numeric representations, and scaling feature values to improve data quality. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied exclusively to the training data. The study evaluates three classifiers, including Logistic Regression as a baseline and two ensemble methods, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). Model performance is examined using several evaluation metrics such as accuracy, precision, recall, F1-score, and the Area Under the ROC Curve (AUC). The results reveal that ensemble learning approaches outperform Logistic Regression, particularly with respect to recall and AUC performance. LightGBM achieves the best overall performance and demonstrates stable predictive capability across all evaluation measures. Feature importance analysis reveals that customer tenure and billing-related attributes, including monthly charges and total charges, are dominant factors influencing churn behavior. These results demonstrate that integrating data balancing techniques with ensemble learning methods offers a robust and effective solution for supporting proactive customer retention initiatives in the telecommunications sector.
Co-Authors Diajeng Tyas Gunawan Gunawan Nur Tulus Ujianto Priyo Haryoko Priyo Haryoko Purwa Hapsari, Diajeng Tyas Raharjo, Gunawan Ujianto, Nur Tulus