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All Journal Jupiter Jurnal Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research INTEGER: Journal of Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informasi dan Komputer Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science JSR : Jaringan Sistem Informasi Robotik Journal of Computer System and Informatics (JoSYC) Journal of Computer Networks, Architecture and High Performance Computing Soeropati: Journal of Community Service Journal of Applied Data Sciences TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Jurnal Publika Pengabdian Masyarakat Jurnal Ilmu Siber dan Teknologi Digital Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) IJISCS (International Journal of Information System and Computer Science)
Triloka, Joko
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Comparison of K-Nearest Neighbor, Naive Bayes, Random Forest Algorithms for Obesity Prediction Andani, Mia; Triloka, Joko; Irianto, Suhendro Yusuf; Nugroho, Handoyo Widi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14478

Abstract

Obesity is a global health problem that continues to increase and has serious impacts on physical and mental health. This research aims to predict a person's obesity status based on certain attributes using the K-Nearest Neighbor (KNN), Naive Bayes, and Random Forest algorithms. The dataset used was taken from the Kaggle platform with 2,111 data and 16 attributes, including gender, age, weight, height, frequency of consumption of high-calorie foods, physical activity, and water and vegetable consumption patterns. The research process follows the data mining stages, including business understanding, data understanding, data preparation, modeling, evaluation, and documentation. Experiments were carried out using RapidMiner with a cross-validation technique using 10 folds to measure overall model performance. The research results show that the Random Forest algorithm performs best in predicting obesity status compared to K-NN and Naive Bayes. Model evaluation using accuracy, precision, recall, and F1-score metrics shows significant results in distinguishing obesity categories. It is hoped that this research can contribute to the development of a machine learning-based health prediction system that can be used to support decision-making in the prevention and management of obesity.
Menu Sales Prediction at Kiyo Café Using Machine Learning Fitriana, Jesi; Triloka, Joko
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3556

Abstract

This research evaluates the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in predicting raw material stock for Café Kiyo. The study encompasses six key stages, including preparation, literature review, data collection, data mining processing, results and discussion, and conclusion with recommendations. The data mining process adheres to the Knowledge Discovery in Databases (KDD) framework, involving data selection, preprocessing, transformation, data mining, and interpretation and evaluation. The evaluation metrics reveal that KNN boasts a marginally higher accuracy of 98.71% compared to Naïve Bayes with 98.21%. KNN also demonstrates superior precision (81.25%) in identifying true positives, outperforming Naïve Bayes (72.59%). However, Naïve Bayes excels in recall, achieving 95.15% compared to KNN's 50.00%. The Area Under the Curve (AUC) analysis further confirms Naïve Bayes' superiority, with an AUC value of 0.995, indicating better performance in distinguishing between positive and negative classes.
Estimasi Jarak Pandang Meteorologi di Bandar Udara Menggunakan Metode Back Propagation dan CNN Maesaroh, Siti; Muludi, Kurnia; Triloka, Joko
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7138

Abstract

Airports in Indonesia often face bad weather problems that affect visibility and impact flight operations. Historical data shows several incidents caused by decreased visibility due to fog or rain that resulted in flight delays and cancellations. It can be said that the importance of more accurate visibility estimates to improve safety and operational efficiency at airports. The purpose of this study was to determine the performance of the Back Propagation and Convolutional Neural Network (CNN) models in estimating meteorological visibility at airports because accurate visibility is very important in determining operational decisions, especially during bad weather conditions. The selection of the Back Propagation method is based on its advantages in handling various types of data dynamically and in a directed manner so that it is more precise in predicting visibility based on interrelated meteorological variables. While Convolutional Neural Network (CNN) is very effective in handling problems involving image data. However, currently there are quite a lot of studies that use CNN for text processing because the results are quite promising. The data used is meteorological data that includes temperature, humidity, air pressure, wind speed and other parameters at Radin Inten II Airport. From the results of this study, the Backpropagation model is better in ROC AUC (85%) compared to CNN (84%), this shows a slight advantage in distinguishing classes. The CNN model is better in Precision by 71% compared to Back Propagation 70%, which means it is slightly better at avoiding false positive predictions. CNN has a higher correlation on the test data (0.20) compared to Back Propagation (0.18) indicating its predictions are slightly more in line with the actual data. The larger correlation difference in CNN (0.18) compared to Back Propagation (0.10) indicates a higher possibility of CNN overfitting compared to BP. Since both models show almost the same performance and the difference is not too significant, the choice of model can depend on the specific needs in the implementation. If the goal is to get a more stable model, then Backpropagation is more recommended because it has a smaller correlation difference and higher ROC AUC. However, if what is sought is a model with more accurate predictions in real scenarios, then CNN can be a better choice because it has higher Precision and better test correlation.
Implementation of Mikrotik Firewall and QoS for Secure and Efficient Internet Networking Seyhan; Triloka, Joko
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.1056

Abstract

Effective network management in school environments is an ongoing challenge as the demand for internet access among students, teachers, and staff continues to grow. This study aims to design and implement a Mikrotik-based network management solution. The research was conducted at SMK Negeri 1 Bandar Lampung, with the objectives of improving network service quality, optimizing bandwidth allocation, and ensuring network security. Mikrotik was chosen due to its flexibility in network management at a relatively affordable cost. The research methods included analyzing the school’s network requirements, designing the network topology, and configuring Mikrotik devices based on Quality of Service (QoS), firewall, and user access control. The results show that the implementation of Mikrotik successfully enhanced network stability, reduced latency, and restricted access to non-relevant websites. These findings highlight that Mikrotik provides an effective and efficient solution for network management, particularly in small to medium-scale settings. This study is expected to serve as a reference for other schools facing similar challenges in network management.
ANALISIS DISTRIBUSI ARAH DAN KECEPATAN ANGIN UNTUK DETEKSI CROSSWIND DI BANDARA RADIN INTEN II MENGGUNAKAN METODE WINDROSE Amidayantik, Damil; Triloka, Joko; Mutiara, Suci
Jurnal Informatika Vol 25 No 2 (2025): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Aviation safety is strongly influenced by wind direction and wind speed, particularly during critical phases of flight such as take-off and landing. One of the most challenging wind conditions for airport operations is crosswind, which occurs when the wind blows perpendicular to the runway direction and can reduce aircraft controllability. Therefore, a comprehensive understanding of wind characteristics around an airport is essential to support safe and efficient flight operations. This study analyzes the distribution of wind direction and wind speed at Radin Inten II Airport using the windrose method as the primary analytical approach, supported by histogram and heatmap visualizations. The dataset consists of historical wind observations collected hourly by the Meteorology, Climatology, and Geophysics Agency (BMKG) Lampung over a ten-year period from 2014 to 2023. The data were processed to identify dominant wind directions, wind speed variability, and periods with a higher potential for crosswind conditions that may disrupt airport operations. The results indicate that the dominant wind direction at Radin Inten II Airport generally originates from the northwest, with prevailing wind speeds ranging between 2 and 6 knots, reflecting relatively stable wind conditions. However, several periods exhibit higher wind speeds that may increase the risk of crosswind occurrences. Further analysis reveals specific months with an increased frequency of crosswind events approaching critical operational thresholds, which require greater attention from airport operators. The findings of this study provide practical recommendations for airport management in determining appropriate runway usage, enhancing situational awareness during high-risk periods, and developing mitigation strategies such as flight schedule adjustments and runway infrastructure improvements. In addition, this research is expected to serve as a reference for other airports with similar wind characteristics in improving aviation safety and operational efficiency.
Analisis Sentimen Publik terhadap Program Makan Bergizi Gratis (MBG) Menggunakan Fine-Tuning Model Pre-trained IndoBERT Rencanawan, Skunda Putra; Triloka, Joko; Saleh, Sushanty
Jurnal Informatika Vol 26 No 1 (2026): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

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Abstract

Abstract — The Free Nutritious Meal Program (MBG) is a strategic policy of the Indonesian government to address stunting and improve human resource quality, yet its implementation has sparked public opinion polarization on social media X. This study aims to analyze public sentiment toward the MBG program to evaluate public responses and provide data-driven insights for policymakers. Traditional machine learning models often struggle to handle semantic complexity and the use of slang in Indonesian social media. Therefore, this study employs the state-of-the-art pre-trained IndoBERT model for sentiment classification. The research methodology involves a comprehensive pipeline implemented within a Graphical User Interface (GUI) application. A total of 3,459 tweets from X were collected, then processed through cleaning steps (removal of URLs, mentions, emojis), normalization using an Indonesian slang lexicon (233 entries), and sentiment labeling (positive, negative, neutral) with manual validation. The IndoBERT model was fine-tuned using 80% training data (2,366 tweets) with the following configuration: epochs = 5, batch size = 16, learning rate = 2e-5, and evaluated on 20% test data (592 tweets). The fine-tuning results achieved an accuracy of 87.16% and an F1-score of 87%, which is significantly higher than previous SVM-based methods (61–63%). Sentiment analysis on the entire cleaned dataset (2,958 tweets) showed a dominance of neutral sentiment (40.8%), followed by negative (30.6%) and positive (28.7%). Word cloud analysis revealed that public support focuses on the words "sehat" (healthy), "anak" (children), "gizi" (nutrition), while negative sentiment centers on "anggaran" (budget), "korupsi" (corruption), and "jakarta". This study concludes that fine-tuned IndoBERT is effective for public policy sentiment analysis on complex Indonesian social media texts, and provides recommendations for the government to improve budget transparency and public communication. The novelty of this study lies in the development of a GPU-accelerated end-to-end desktop application that integrates the entire Natural Language Processing pipeline, making it a practical real-time evaluation tool for non-technical government analysts.   Keyword — IndoBERT, Natural Language Processing, Public Policy, Sentiment Analysis, Free Nutritious Meal Program
Co-Authors Abda Abda Amidayantik, Damil Andani, Mia Andika, Tahta Herdian Aras, Irsan Arie Ardiansyah, Arie Aris Kurniawan Assidik, Reksa Qodri Aziz, RZ. Abdul Badri, Rico Elhando Chairani Chairani Dewi, Deshinta Arrova Effendi, Mukhammad Khoirul Eko Setiawan, Agustinus Fadila, Kurnia Fitriana, Jesi Goesderilidar handoyo widi nugroho Hartono Hartono Indera Indera Kurnia Muludi Kurniawan Kurniawan Kurniawan, Tri Basuki Kurniawan, Wildan Indra M Said Hasibuan Maizary, Ary Mutiara, Suci Nouvan, M. Ahadi Novita Sari Nur ‘Azah Pramodiyo Wilantara, Rangga Priyatna, Aziz R Rizal Isnanto Rachmadi, Rachmadi Rencanawan, Skunda Putra Rintiana, Rintiana Rio Kurniawan Saputra, Muhammad Seyhan Siti Maesaroh Sugianto, Dian Sushanty Saleh Sutedi Sutedi Tarjono Wasilah Wasilah Wibaselppa, Anggawidia Y. Suhendro Yusuf Irianto, Suhendro