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All Journal TEKNIK INFORMATIKA Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JAIS (Journal of Applied Intelligent System) Creative Information Technology Journal SISFOTENIKA JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI CCIT (Creative Communication and Innovative Technology) Journal Jurnal Sistem Informasi Kaputama (JSIK) E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) IT (INFORMATIC TECHNIQUE) JOURNAL Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Jurnal Dharma Bhakti Ekuitas Journal of Social Responsibility Projects by Higher Education Forum INFOSYS (INFORMATION SYSTEM) JOURNAL Jurnal Minfo Polgan (JMP) Jurnal ABDIMAS Budi Darma Journal of Artificial Intelligence and Engineering Applications (JAIEA) INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Information System and Data Science Information Technology and Cyber Crime Informatics Engineering and Electronic Data Jurnal Penelitian Sistem Informasi Jurnal ICT : Information and Communication Technologies Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka Jurnal Bima PUBLIDIMAS ORAHUA : Jurnal Pengabdian Kepada Masyarakat
Lili Tanti
Universitas Potensi Utama
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Penerapan Kombinasi Random Forest dan CNN untuk Mendeteksi Kecurangan Tagihan Listrik di Ulp Telda Albert Putra Nias Manao; Wanayumini Wanayumini; Lili Tanti
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16002

Abstract

Electricity fraud remains a major challenge for PLN (State Electricity Company), causing significant financial losses and reducing service reliability. This study aims to develop an electricity bill fraud detection system at PLN ULP Telda using a hybrid approach that combines Random Forest and Convolutional Neural Network (CNN) algorithms. Random Forest is effective in handling structured tabular data and identifying important features, while CNN excels in recognizing complex patterns in time-series electricity consumption data. By integrating both algorithms, the system is expected to achieve higher accuracy and reliability compared to a single model. The research methodology includes data collection from smart meters, pre-processing, feature extraction, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. Initial results indicate that the hybrid model improves detection performance, reduces false negatives, and strengthens fraud identification. The final results show that the combined system outperforms the single model, with an accuracy of 91.3%, a precision of 89.7%, a recall of 88.4%, and an ROC-AUC of 90.5%. This research contributes to PLN's ability to detect fraud more accurately and efficiently. The contribution of this research is financial efficiency for PLN, fairer electricity tariffs for customers, and a reference for further research in the application of machine learning for energy fraud detection.
Peningkatan Keamanan Data Digital Dengan Pendekatan Kombinasi Algoritma Kriptografi OTP dan Cramer Shoup Gilang Dwi Fahri Harahap; Budi Triandi; Lili Tanti
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16080

Abstract

Keamanan data digital menjadi aspek krusial seiring meningkatnya ancaman siber terhadap kerahasiaan dan integritas informasi. Penelitian ini bertujuan untuk meningkatkan keamanan data digital melalui pendekatan kombinasi algoritma kriptografi One-Time Pad (OTP) dan Cramer-Shoup dalam sebuah skema hybrid. Algoritma OTP memiliki keunggulan dalam menjaga kerahasiaan data secara teoritis, namun memiliki kelemahan pada distribusi kunci dan potensi serangan seperti Known-Plaintext Attack (KPA). Sementara itu, algoritma Cramer-Shoup sebagai kriptografi asimetris menawarkan keamanan yang kuat terhadap serangan adaptif serta mendukung autentikasi data. Metode penelitian yang digunakan meliputi perancangan sistem hybrid, implementasi algoritma menggunakan bahasa pemrograman Python, serta pengujian keamanan dan kinerja sistem. Proses enkripsi dilakukan dengan mengombinasikan OTP untuk penyandian pesan dan Cramer-Shoup untuk pengamanan distribusi kunci. Evaluasi dilakukan melalui simulasi serangan KPA dan analisis perbandingan waktu proses enkripsi-dekripsi. Hasil penelitian menunjukkan bahwa kombinasi kedua algoritma mampu meningkatkan tingkat keamanan data secara signifikan, terutama dalam mengatasi kelemahan distribusi kunci pada OTP dan meningkatkan ketahanan terhadap serangan KPA. Selain itu, sistem hybrid yang diusulkan tetap mempertahankan efisiensi kinerja yang baik. Dengan demikian, pendekatan kombinasi OTP dan Cramer-Shoup dapat menjadi solusi efektif dalam meningkatkan keamanan data digital serta memberikan kontribusi dalam pengembangan sistem kriptografi yang lebih adaptif dan andal.
Optimalisasi Akurasi Naïve Bayes Menggunakan Seleksi Atribut Relief-F dan Gain Ratio Agung RM Alam; Wanayumini Wanayumini; Lili Tanti
Jurnal Minfo Polgan Vol. 15 No. 2 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i2.16112

Abstract

Naïve Bayes Classifier (NBC) merupakan salah satu algoritma klasifikasi probabilistik yang paling banyak digunakan dalam data mining karena kesederhanaan dan efisiensinya. Namun, performa NBC cenderung menurun ketika dataset mengandung atribut yang tidak relevan atau mengandung noise. Penelitian ini bertujuan menganalisis pengaruh seleksi atribut menggunakan metode Relief-F dan Gain Ratio terhadap peningkatan akurasi NBC. Dua dataset dari UCI Machine Learning Repository digunakan sebagai bahan pengujian: dataset House Vote (435 data, atribut simbolik) dan dataset Bank Marketing (45.211 data, atribut numerik dan kategorikal). Tiga skenario eksperimen diterapkan pada masing-masing dataset: (1) NBC tanpa seleksi atribut sebagai baseline, (2) NBC dengan seleksi atribut Relief-F, dan (3) NBC dengan seleksi atribut Gain Ratio. Evaluasi performa menggunakan 10-fold cross-validation dengan metrik akurasi, presisi, recall, F1-score, dan confusion matrix. Hasil penelitian menunjukkan bahwa pada dataset House Vote, Relief-F berhasil meningkatkan akurasi NBC dari 90,11% menjadi 93,79% (+3,68%), sedangkan Gain Ratio justru menurunkan akurasi menjadi 89,43%. Pada dataset Bank Marketing, Relief-F meningkatkan akurasi menjadi 89,36% dan memperbaiki recall kelas minoritas dari 29,34% menjadi 35,71%, sementara Gain Ratio hanya memberikan peningkatan marginal. Secara keseluruhan, Relief-F terbukti lebih efektif dibandingkan Gain Ratio dalam meningkatkan performa NBC, khususnya pada dataset dengan pola klasifikasi yang jelas dan distribusi kelas yang tidak seimbang.
Extending Hybrid GRG-NS With LSTM-Based Demand Forecasting for Dynamic Multi-Depot Routing in Disaster Logistics Dedy Hartama; Poningsih Poningsih; Lili Tanti
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1444

Abstract

Disaster logistics management requires accurate demand forecasting and efficient routing optimization to ensure timely distribution of emergency supplies under dynamic and uncertain conditions. Conventional routing approaches often experience limitations in handling fluctuating disaster demand, resulting in inefficient distribution performance and increased operational costs. This study proposes an integrated LSTM–Hybrid Generalized Reduced Gradient and Neighborhood Search (LSTM–Hybrid GRG–NS) framework for disaster-demand forecasting and routing optimization. The proposed approach combines Long Short-Term Memory (LSTM) for sequential demand prediction with a hybrid GRG–NS optimization mechanism to improve routing efficiency and solution convergence. Experimental evaluation was conducted using disaster-demand scenarios and routing datasets to assess forecasting and optimization performance. The forecasting results demonstrated strong predictive capability with low MAE, RMSE, and MAPE values, indicating that the LSTM model effectively captured temporal demand patterns. Furthermore, the routing optimization results showed that the proposed framework successfully generated stable and near-optimal routing solutions while maintaining full demand fulfillment and efficient vehicle utilization. The convergence analysis also confirmed that the optimization process converged consistently within a limited number of iterations. Overall, the proposed LSTM–Hybrid GRG–NS framework provides an effective and reliable decision-support approach for proactive humanitarian logistics and disaster-routing management.
Performance Comparison of Decision Tree, KNN, and Naive Bayes for Air Quality Classification Yan Yang Thanri; Juli Iriani Iriani; Lili Tanti Tanti; Luthfi Zaidi Zaidi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5121

Abstract

Air quality degradation has become a critical environmental and public health issue, necessitating accurateand reliable classification models to support effective monitoring systems. This study aims toconduct a comparative analysis of four machine learning algorithms-Decision Tree, k-Nearest Neighbor (kNN), Naive Bayes, and Stochastic Gradient Descent (SGD)-for classifying air quality using environmental parameters, including particulate matter ≤ 2.5 μm (PM2.5), carbon monoxide (CO), temperature, humidity, nitrogen dioxide (NO2), and sulfur dioxide (SO2). The methodology employssupervised learning, where each model is trained and evaluated using classification accuracy, area under the receiver operating characteristic curve (AUC), F1-Score, precision, recall, and Matthews Correlation Coefficient (MCC), supported by ROC curve and confusion matrix analyses. The results show that the Decision Tree algorithm achieves the best overall performance, attaining a classification accuracy of 93.8% with a balanced precision, recall, and F1-Score, indicating strong and consistent predictive capability. The kNN and Naive Bayes models record the highest AUC values (0.980 and 0.982, respectively), demonstrating excellent class separability, although their accuracy and F1-Score are lower than those of the Decision Tree. In addition, the SGD model, implemented with a modified Huber loss function and L2 regularization, provides interpretable feature-weight analysis, identifyingPM2.5 and CO as dominant indicators of the Hazardous air quality class, while temperature and humidity significantly influence the Fair and Good classes. Based on the comprehensive evaluation, the Decision Tree algorithm is recommended as the most reliable model for accurate air quality classification, whereas the SGD model is particularly suitable for feature contribution analysis to enhance interpretability. These findings offer practical insights for selecting appropriate machine learning models in air quality monitoring and decision-support systems.
Co-Authors adhar, Deni Adhar, Deni Adhar Agung RM Alam Ahmad, Ahmad Syah Lubis Ahsanul Huda Albert Putra Nias Manao Alim Murtani Alvian Julianto Hutajulu alya, Alya Rahmadani Andra Alfira Andra Alfitra Andrian Syahputra Anggi, Anggi Canita Simanjuntak Ayu Nadya Ayuni Syahputri Aziz Ritonga, Mirwan Bob Subahan Riza Bob Subhan Riza Bob Subhan Riza Bob Subhan Riza Bob Subhan Riza, Bob Subhan Budi Triandi Budi Triandi Budi Triandi, Budi Daifiria Daifiria Dedy Hartama Deni Adhar Deni Adhar Deni Adhar Adhar Deni Anggara Devi Pratiwi Putri Dewi Kartika Dhooni, Dhoni Briliant Efendi, Syahril Erica Rian Safitri Evri Ekadiansyah fachrie, Fachrie Ditya Faisal Tanjung Fauzan Arif Feberianus Zai Fretty S Siahaan Gilang Dwi Fahri Harahap Handoko, Muhammad Yan Handoko Putra F Hartama, Dedy Hartati Tammamah Lubis Herman Mawengkang Indah Widiastuti Iwan Fitrianto Rahmad Jasri, Jasri Ramadhan Juli Iriani Juli Iriani Juli Iriani Juli Iriani Iriani Juli Iriani, Juli Juni Ismail Khairul Fajri Khairul Ummi Lahmudin Sipahutar Lubis, Hartati Tammamah Luthfi Zaidi Zaidi M Rizky M Zendi Lubis M. Aidil Fitra Wahyudi M. Haidil Umam Mangunsong, Puja Mawardah Azzahra Maya Silvi Lydia Muhammad Daud Muhammad Faris Nanda Setiawan Nety Juwita Lubis Nurainun Nurhayati Nurhayati Pairin, Yusfrizal Bin Patuan Putra Wijaya Sitorus Poningsih Poningsih Poningsih Poningsih, Poningsih Rabiana Saragih Ratih Puspasari Ratih Puspasari Ratih, Ratih Puspasari Ridho, Irdian Riza, Bob Subahan Rofiqoh Dewi Roslina Roslina Roslina Roslina, Roslina Safitri, Erica Rian Safrizal Safrizal Safrizal Safrizal Safrizal Safrizal Safrizal Salwani D, Zuki Salwani D Simalango, Clarensia Mende Surbakti, Dio Febrian Susianto Susianto Syefira Arrafah Tasya Ardilah Thanri, Yan Yang Wanayumini Windy, Micky Alviansyah Wirhan Fahrozi, Wirhan Yanyang Thanri Yudhi Andrian Yulika Ababil _, Safrizal