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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

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.

Impact of The Covid-19 Pandemic on Student Learning Styles: Naïve Bayes and Decision Tree Classification in Education Kurniawan, Zaqi; Tiaharyadini, Rizka
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1950

The Covid-19 pandemic significantly changed education with social distancing and changes in the learning environment. In this study, one strong reason for the significance of the research is the urgency of changes in students' learning styles during the Covid-19 pandemic. Investigating differences in learning styles before and during the pandemic not only provides deep insight into students' adaptation to these changes, but also provides a foundation for the development of more inclusive and adaptive learning strategies in the future. This study aims to analyze the effect of the Covid-19 pandemic on students' learning styles in an educational context, focusing on the comparison of two classification methods, Naïve Bayes and Decision Tree. The study was conducted by collecting data on students' learning styles before and during the Covid-19 pandemic, using various relevant indicators. The data was obtained based on school survey results and online platforms, involving student characteristics and learning preferences. The data was then analyzed using Naïve Bayes and Decision Tree classification methods to identify significant changes in students' learning styles. The results showed the prediction accuracy of learning style changes with Naïve Bayes 68.75% and Decision Tree 87.50%. Recommendations for educators and education policy makers are to develop inclusive and adaptive learning strategies to meet diverse learning preferences.

ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS Zaqi Kurniawan; Rizka Tiaharyadini; Puguh Jayadi; Windhy widhyanty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7344

Climate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production.

ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS Zaqi Kurniawan; Rizka Tiaharyadini; Puguh Jayadi; Windhy widhyanty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7344

Climate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production.

Deep Learning-Based Detection and Classification of Rice Leaf Diseases Using ResNet-50 with Augmentation and K-Fold Cross-Validation Zaqi Kurniawan; Rizka Tiaharyadini; M Saddam Ryuga Octoramdhani; Radiz Dirgantara
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12716

Rice sustains over half of the global population; however, leaf diseases such as blight, brown spot, and leaf smut significantly reduce crop yields. Early and accurate detection is essential for supporting sustainable agriculture practices. This study proposes a ResNet-50-based deep learning model for rice leaf diseases classification using a dataset of 1,150 field images collected from Sleman, Indonesia, which was expanded to 2,000 images through augmentation techniques, including rotation, flipping, zooming, and brightness adjustment. Model performance was evaluated using both hold-out validation and 10-fold cross-validation with accuracy, precision, recall, and F1-Score metrics. The application of data augmentation improved hold-out validation accuracy from 82.4% to 88.1%. Meanwhile, 10-fold-cross-validation yielded a substantially higher average accuracy of 99.6%. This discrepancy suggests potential sensitivity to data partitioning and indicates the need for careful interpretation, as cross-validation may introduce optimistic estimates under certain conditions. Although the proposed approach demonstrates strong performance in distinguishing visually similar diseases, this study limited by the use of a single-region dataset, which may affect generalizability. Therefore, the integration of ResNet-50, augmentation, and cross-validation shows promising results for early disease detection, while further validation on more diverse datasets is required to support it application in real-world precision agriculture systems.

Topic Modelling of TikTok Application User Complaints in Google Play Store Reviews Using Latent Dirichlet Allocation Ghevira Devanda; Inaya Zehan Kalyzta; Zaqi Kurniawan
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i5.33

TikTok is a short-video-sharing platform with an extremely large user base in Indonesia, and as a result it also receives numerous negative reviews concerning both technical performance and platform policies on the Google Play Store. This study aims to identify and cluster the main topics of TikTok user complaints using a Machine Learning-based approach built on the Latent Dirichlet Allocation (LDA) algorithm. Data were collected through a quota-based web scraping procedure using the google-play-scraper library, targeting Indonesian-language reviews with a rating below three (rating 1 and 2) up to a target of 1,000 reviews, yielding a raw sample of 1,000 reviews (814 one-star and 186 two-star). The preprocessing stage included cleaning, case folding, normalization using a colloquial lexicon enriched with a TikTok domain-specific correction dictionary, stopword removal, and stemming using Sastrawi, which reduced the data to 814 clean documents. Document representation was constructed using a Bag of Words (BoW) approach through the Gensim library, with vocabulary filtering (no_below = 3, no_above = 0.6) that produced a dictionary consisting of 397 unique tokens. The optimal number of topics was determined through a multi-metric evaluation combining Coherence Score (u_mass), Coherence Score (c_v), and Perplexity for candidate models ranging from two to five topics, complemented by a manual check of topic interpretability. Despite mixed signals from the c_v and Perplexity metrics, the two-topic model was selected based on its u_mass score (-8.7713) and its more interpretable, non-fragmented thematic structure. The document distribution results show that Topic 1 is more dominant, comprising 445 documents (54.67%), representing technical application complaints such as bugs, slow performance, failure to open the application, and excessive advertisements. Meanwhile, Topic 2, with 369 documents (45.33%), relates to complaints about content moderation and restrictions on the live streaming feature. Visualization using pyLDAvis confirmed that the two topics are clearly separated without overlap, reinforcing the validity of the modelling results. As the sample is restricted to Indonesian-language, one- and two-star reviews collected at a single point in time, the findings are intended to reflect this specific segment of TikTok users rather than the full population of TikTok reviews. This study provides objective insights for application developers in prioritizing system improvements and evaluating content moderation policies more effectively.

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