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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Sisfokom (Sistem Informasi dan Komputer) Indonesian Journal of Computing, Engineering, and Design Jurnal Pengabdian Masyarakat Tekno Borneo Community Development
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Arts Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering

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Optimizing Malware Detection and Prevention on Proxy Servers Through Random Forest and Lexical Feature Analysis Andalas Saputra, Meitro Hartanto; Pebrianti, Dwi; Bayuaji, Luhur; Rusdah
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 7 No. 1 (2025): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v7i1.485

Abstract

Malware has become a significant concern due to the increase in malicious websites hosting spam, phishing, malware, and other threats. This research aims to predict malware URLs using lexical features for feature extraction and random forest for classification. The dataset, sourced from kaggle.com, includes benign, phishing, spam, malware, and defacement URLs. To address data imbalance, random oversampling was applied for balanced training. Recursive feature elimination was used to optimize lexical features, testing various sets of features (10, 15, 19, 23, 29, 35) for classification accuracy, achieving 98% accuracy using 23 features. Validation tests with actual university network data confirmed this model’s effectiveness, classifying malicious URLs in 9 minutes using 11,566 samples. URL filtering involved log analyzer tools capturing internet traffic during working hours over one month. Results suggest that this approach can efficiently classify malicious URLs and could be implemented for real-time detection in proxy server logs, aiding IT departments in preventing malware spread via web traffic.
Education on Treatment of Gastric Disorders, Self-Medication of Gastric Medicines, and DAGUSIBU in Karang Bunga Village Yulianita Pratiwi Indah Lestari; Rusdah; Agustina Tri Wahyuni; Nor Azizah Rahmah Sari; Nor Syifa
Borneo Community Development Vol. 5 No. 1 (2025)
Publisher : UMBanjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35747/bcd.v5i1.1185

Abstract

Gangguan lambung seperti maag, gastritis, dan tukak lambung merupakan masalah kesehatan yang umum di Indonesia, dengan prevalensi sekitar 10-15% penduduk (Riskesdas 2018). Faktor pemicunya meliputi pola makan yang tidak sehat, stres, konsumsi alkohol, merokok, serta infeksi Helicobacter pylori. Swamedikasi sering dilakukan oleh masyarakat dalam menangani gangguan lambung dengan membeli obat tanpa resep dokter, namun kurangnya pemahaman mengenai penggunaan obat yang benar dapat meningkatkan risiko efek samping dan komplikasi. Oleh karena itu, edukasi mengenai swamedikasi yang aman sangat penting untuk meningkatkan kesadaran masyarakat. Kegiatan pengabdian ini bertujuan untuk memberikan penyuluhan kepada masyarakat Desa Karang Bunga mengenai penggunaan obat lambung yang benar berdasarkan prinsip DAGUSIBU (Dapatkan, Gunakan, Simpan, Buang). Penyuluhan dilakukan kepada 28 peserta, yang terdiri dari ibu-ibu PKK dan kader desa Karang Bunga, dengan metode ceramah interaktif serta diskusi mengenai klasifikasi obat, aturan penggunaan yang tepat, serta cara penyimpanan dan pembuangan obat yang benar. Hasil kegiatan menunjukkan bahwa sebelum penyuluhan, sebagian besar peserta belum memahami prinsip DAGUSIBU, terutama dalam hal mendapatkan obat dengan benar, menentukan dosis yang tepat, serta menyimpan dan membuang obat secara aman. Setelah penyuluhan, terjadi peningkatan pemahaman yang signifikan terkait aspek-aspek tersebut. Kegiatan edukasi ini diharapkan dapat mengurangi risiko penyalahgunaan obat serta meningkatkan kualitas kesehatan masyarakat, sehingga perlu dilakukan secara berkelanjutan untuk memastikan dampak jangka panjang yang lebih optimal.
Pemanfaatan Google Meet Dan Classroom Pada Media Pembelajaran Di PKBM Bina Bangsa Ratna Kusumawardani; Rusdah; Setiono, Devit; Kusumaningsih, Dewi; Syafrullah, Muhammad
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 5 No 1 (2024): Juni 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v5i1.5533

Abstract

Currently, learning media in Indonesia uses ICT-based learning media. This is because all learning institutions and educational institutions apply blended learning, namely offline (offline) and online (online) in the process of teaching and learning activities. Using good and appropriate learning media can make it easier for students to digest learning material. One of the educational institutions that carries out online teaching and learning activities is PKBM Bina Bangsa Tangerang. This institution has a role in training people's skills and independence. However, some students have not mastered using Google Meet and Google Classroom in teaching and learning activities. To fulfill this, we are collaborating with PKBM Bina Bangsa Tangerang to conduct training on using Google Meet and Google Classroom. This activity aims to ensure that students understand how to use Google Meet and Google Classroom in the teaching and learning process so that students can receive the material provided by the teacher well. This training applies a practicum method involving 16 students which was carried out online on October 21, 2023. Based on the questionnaire data that has been processed, it shows that this activity was well received by the students and the students understood the facilities available. found in Google Meet as much as 90% and understanding regarding the use of Google Meet as much as 91%. Then the students' understanding regarding the facilities available in Google Classroom was 87% and their understanding regarding the use of Google Classroom was 86%
Trend Analysis and Prediction of Violence Against Women and Children Cases in Jakarta Based on the Victim’s Education Level Using ARIMA and SARIMA Method Kurniawan, Zaqi; Tiaharyadini, Rizka; Wibowo, Arief; Rusdah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2349

Abstract

Violence against women and children remains a critical social issue in Jakarta, Indonesia, where densely populated urban areas often correlate with increased risks of domestic abuse. The urgency of addressing this problem lies in its direct impact on public health, education, and community well-being. This study uses time series prediction models to examine and anticipate trends in the number of reported incidents of violence against women and children in Jakarta. Using publicly accessible data from Jakarta Open Data and the National Commission for the Protection of Women and Children, we applied the ARIMA and SARIMA  Models. Key variables included in the dataset are the data period, education level, and total number of victims Using three performance indicators—MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error)—to assess model accuracy the ARIMA model performed better than the SARIMA model. SARIMA recorded an RMSE of 80.26, an MAE of 66.21, and an undefined MAPE because of zero values in the real data, while ARIMA specifically obtained an RMSE of 32.22, an MAE of 32.09, and a MAPE of 5.19%. These results suggest that the non-seasonal ARIMA model is more suitable for this dataset. The study contributes to policy planning and early intervention strategies by offering a data-driven approach to predicting trends in violence within urban contexts.
Predicting Early Lease Termination Risk in Jakarta Shopping Malls Using a SMOTE-Enhanced SVM Model for Financial Loss Prevention Syarifuddin Abdullah , Andi; Rusdah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30980

Abstract

The high incidence of early lease termination in shopping malls poses significant challenges to revenue generation, unit utilization, and the operational stability of commercial properties. The limitations of traditional management practices in identifying high-risk tenants early often result in financial losses and suboptimal asset allocation. To address this issue, this study developed a data-driven predictive model designed to identify the likelihood of early lease termination. The approach integrates the Support Vector Machine (SVM) algorithm with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance within the dataset. The model development followed the CRISP-DM methodology and utilized a historical dataset comprising 795 lease records from a major shopping mall in Jakarta, spanning the years 2015 to 2022. Through systematic data preprocessing, feature selection, and model optimization using grid search and cross-validation, the model achieved excellent classification performance: 93.10% accuracy, 90.50% precision, 96.40% recall, 93.30% F1-score, and 97.30% AUC. The findings demonstrate that the SMOTE–SVM combination consistently outperforms in detecting minority-class cases. A prototype system was also developed, enabling mall managers to predict tenant risk in real-time through an intuitive user interface. The contributions of this research are twofold. First, it presents a novel application of the SMOTE–SVM approach for addressing data imbalance in early lease termination prediction within the Indonesian commercial property sector an area that remains underexplored. Second, the study delivers a practical and deployable prototype system that enables real-time risk assessment for mall management, thereby bridging the gap between predictive modeling and operational decision-making. Overall, the proposed model offers a reliable and scalable predictive solution that can be adapted for risk management in other commercial property contexts, supporting a data-driven and proactive decision-making approach. However, it is important to note that the applicability of the proposed SMOTE–SVM model may face certain challenges when deployed in different commercial property contexts. Variations in tenant characteristics, market dynamics, economic conditions, and data availability across regions could impact model generalizability and performance. Moreover, the reliance on historical lease data assumes consistency in tenant behavior patterns, which may not hold true in rapidly evolving retail environments or for properties with distinct operational models such as coworking spaces or mixed-use developments. These factors should be carefully considered when adapting the model to ensure its validity and effectiveness outside the original study setting.
Co-Authors Agustina Tri Wahyuni Andalas Saputra, Meitro Hartanto Arief Wibowo Devit Setiono Dewi Kusumaningsih Luhur Bayuaji, Luhur Nor Azizah Rahmah Sari Nor Syifa Pebrianti, Dwi Ratna Kusumawardani Syafrullah, Muhammad Syarifuddin Abdullah , Andi Tiaharyadini, Rizka Yulianita Pratiwi Indah Lestari