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All Journal JIKO (Jurnal Informatika dan Komputer) Jambura Journal of Informatics Jurnal Ilmiah Manajemen Publik dan Kebijakan Sosial Journal of Sustainable Tourism and Entrepreneurship Jurnal Teknik Informatika (JUTIF) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Eduvest - Journal of Universal Studies Jurnal Abdimas Sosek (Jurnal Pengabdian dan Pembardayaan Masyarakat) Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Digitus : Journal of Computer Science Applications
Muhammad Alfathan Harriz
Universitas Matana
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences

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Enhancing the Efficiency of Jakarta's Mass Rapid Transit System with XGBoost Algorithm for Passenger Prediction Muhammad Alfathan Harriz; Nurhaliza Vania Akbariani; Harlis Setiyowati; Handri Santoso
Jambura Journal of Informatics VOL 5, NO 1: APRIL 2023
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v5i1.18814

Abstract

This study is based on a machine learning algorithm known as XGBoost. We used the XGBoost algorithm to forecast the capacity of Jakarta's mass transit system. Using preprocessed raw data obtained from the Jakarta Open Data website for the period 2020-2021 as a training medium, we achieved a mean absolute percentage error of 69. However, after the model was fine-tuned, the MAPE was significantly reduced by 28.99% to 49.97. The XGBoost algorithm was found to be effective in detecting patterns and trends in the data, which can be used to improve routes and plan future studies by providing valuable insights. It is possible that additional data points, such as holidays and weather conditions, will further enhance the accuracy of the model in future research. As a result of implementing XGBoost, Jakarta's transportation system can optimize resource utilization and improve customer service in order to improve passenger satisfaction. Future studies may benefit from additional data points, such as holidays and weather conditions, in order to improve XGBoost's efficiency.
CLASSIFYING VILLAGE FUND IN WEST JAVA, INDONESIA USING CATBOOST ALGORITHM Muhammad Alfathan Harriz; Nurhaliza Vania Akbariani; Harlis Setiyowati; Handri Santoso
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 2 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i2.269

Abstract

With over 261 million inhabitants, Indonesia is home to approximately 15,000 villages, according to the Ministry of Villages, Disadvantaged Regions, and Transmigration. Among these, 1,406 are in West Java. Of these, 504 of them are advanced, 464 are developing, 390 are disadvantaged, and 48 are very disadvantaged. The CatBoost machine learning model was used to classify village funds in West Java from 2018 to 2021 and had an accuracy rating of 75%, precision rating of 79%, recall of 79%, and f1 score of 79%, demonstrating its excellent performance. However, missing data points had to be removed from the analysis and it is suggested that a more sophisticated method for handling missing values should be used in future studies. In addition, hyperparameter tuning could be employed to increase the model's performance, and a variety of metrics could be used to accurately assess the results. Overall, CatBoost may be of benefit to the Indonesian Government in order to classify village funds according to their status, channel funds more accurately and efficiently, and observe the situation of a village year-over-year.
Tantangan Pemimpin Perempuan pada Rehabilitas Narkoba Trisna Health Voluntary Center Tulungagung Harlis Setiyowati; Muhammad Alfathan Harriz; Indah Sulistyoningsih; Nurhaliza Vania Akbariani
Jurnal ilmiah Manajemen Publik dan Kebijakan Sosial Vol 6 No 2 (2022): Jurnal Ilmiah Manajemen Publik dan Kebijakan Sosial
Publisher : Universitas Dr. Soetomo Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/jmnegara.v6i2.5016

Abstract

The success of a female leader will depend on her self-confidence in her abilities and abilities. Women leaders who are business owners often have a strong drive to be able to realize their potential and contribute to society. The Trisna Health Voluntary Center (HVC) Drug Victims Rehabilitation Foundation is run by Mrs. Triswati Sasmito. The HVC Foundation offers social rehabilitation as well as assistance or recovery to substance misuse victims. 1) How to engage and participate in challenges as a female leader is the primary focus of this study. 2) Women leaders' management and implementation of drug rehabilitation initiatives experience. 3) Observe the difficulties faced by female executives in other industries. The conclusions include: 1) How to connect and participate in challenges as a female leader, specifically with a deep touch such that it has a favorable impact on the anticipated results, including speedier recovery and regaining health and even being able to join with family and the local community. 2) The expertise of female company owners in the field of drug rehabilitation, namely in the administration and execution of therapeutic activities utilizing Therapeutic Communities and running Drug Prevention Education (DPE) programs 3) Women leaders in different industries have encountered a variety of difficulties, depending on their respective fields and nations, but they have managed to persevere and keep up their good work. Women still don't participate enough in politics. To donate their own quotas for national growth, it is advised that more women participate.
KOMPARASI ALGORITMA DECISION TREE DAN KNN DALAM MENGKLASIFIKASI DAERAH BERDASARKAN PRODUKSI LISTRIK Muhammad Alfathan Harriz; Harlis Setiyowati
JURNAL INFORMATIKA DAN KOMPUTER Vol 7, No 2 (2023): SEPTEMBER 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v7i2.787

Abstract

Pilihan negara yang sedang tumbuh dan berkembang, memiliki jumlah penduduk yang besar dan luas wilayah yang luas yaitu India sebagai contoh untuk diteliti. Listrik komponen vital dan berperan penting. Peneliti melakukan perbandingan akurasi antara dua algoritma pembelajaran mesin yang populer, yaitu Decision Tree dan KNN (K-Nearest Neighbor). Dataset yang berisi sampel sebanyak 345273 digunakan dan  validasi dengan metode StratifiedKFold sebanyak 33 bagian dilakukan untuk mengevaluasi hasil klasifikasi dari kedua algoritma tersebut. Hasil dari penelitian ini menunjukkan bahwa algoritma Decision Tree yaitu 85.78% dan memiliki akurasi yang lebih tinggi dibandingkan dengan KNN yang memiliki akurasi sebesar 80.34% dalam mengklasifikasi daerah di India berdasarkan produksi listrik yang dihasilkan. Selain itu, temuan lainnya yaitu Decision Tree memiliki waktu komputasi yang lebih cepat yaitu 51.66 detik dibandingkan dengan KNN yang memiliki waktu komputasi sebesar 56.27 detik. Kesimpulan dari penelitian ini adalah bahwa algoritma Decision Tree memiliki akurasi yang lebih tinggi dibandingkan dengan algoritma KNN. Selain itu, Decision Tree juga memiliki waktu komputasi yang lebih cepat. Dengan demikian, algoritma Decision Tree dapat menjadi pilihan yang lebih baik dalam melakukan klasifikasi.
A COMPARISON OF THE NAIVE BAYES AND K-NN ALGORITHMS IN PREDICTING THE FRESHNESS OF MILKFISH AT FISH AUCTIONS Harlis Setiyowati; Hendra Mayatopani; Lilik Hariyanto; Muhammad Alfathan Harriz
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2277

Abstract

This research aims to compare the performance of two machine learning algorithms, Naive Bayes and K-Nearest Neighbors (K-NN), in predicting the freshness of milkfish (Chanos chanos) at fish auctions. Predicting fish freshness is an important aspect to ensure product quality and customer satisfaction. The Naive Bayes algorithm, which is based on Bayes' Theorem with the assumption of independence between features, as well as the K-NN algorithm, which uses an instance-based approach to classify data based on proximity to k nearest neighbors, were implemented and tested. Evaluation is carried out using accuracy and Kappa metrics. The results show that Naive Bayes achieved an accuracy of 73.44% with a Kappa value of 0.594, indicating good performance in predicting the freshness of milkfish. In contrast, K-NN shows an accuracy of 68.75% and a Kappa value of 0.461, which means its performance is lower compared to Naive Bayes. Further analysis revealed that Naive Bayes is more computationally efficient and faster at making predictions, making it better suited for real-time applications at fish auctions. However, Naive Bayes has limitations in assuming feature independence which may not always be true in real-world situations. On the other hand, K-NN although more flexible and capable of capturing complex patterns in data, tends to be slow and requires optimization of parameters such as k values ​​to improve its performance. In conclusion, Naive Bayes is recommended for predicting the freshness of milkfish at fish auctions because of its higher accuracy and reliability. Further research is needed to optimize these two algorithms through parameter adjustments and the use of ensemble methods to improve overall prediction performance.
KUNJUNGAN EDUKATIF MAHASISWA PROGRAM STUDI MANAJEMEN RITEL KE MAS MIRACLE FARM TAPOS, DEPOK Harlis Setiyowati; Luh Putu Puji Trisnawati; Muhammad Alfathan Harriz; Nurhaliza vania Akbariani
Jurnal Abdimas Sosek (Jurnal Pengabdian dan Pemberdayaan Masyarakat Sosial Ekonomi) Vol. 4 No. 2 (2024)
Publisher : Departemen Pengabdian Masyarakat Perkumpulan Dosen Manajemen Indonesia (PDMI)

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

Abstract

Kunjungan edukatif mahasiswa Program Studi Manajemen Ritel ke Mas Miracle Farm Tapos, Depok, merupakan inisiatif dalam rangka pengabdian masyarakat yang bertujuan untuk memahami praktik pertanian organik dan manajemen ritel yang berkelanjutan. Studi dilakukan melalui observasi langsung, wawancara dengan pengelola farm dan pekerja, survei terhadap masyarakat dan pengunjung, Hasil kunjungan mengungkapkan bahwa Mas Miracle Farm menerapkan praktik pertanian organik dengan fokus pada penggunaan pupuk kompos, tanpa pestisida dan pupuk kimia, serta manajemen air yang efisien. Farm juga aktif dalam pemberdayaan masyarakat melalui pelatihan pertanian organik dan penciptaan lapangan kerja lokal. Strategi pemasaran farm melibatkan penjualan langsung ke konsumen dan kemitraan dengan toko ritel, didukung oleh upaya membangun kesadaran akan manfaat produk organik melalui media sosial dan platform digital. Mahasiswa mendapatkan pemahaman mendalam tentang pentingnya praktik pertanian berkelanjutan dan manajemen ritel yang efektif. Implikasi praktis bahwa mahasiswa memiliki alternatif bisnis dengan mengaplikasikan pengetahuan yang diperoleh dalam pengembangan karir mereka di sektor pertanian dan ritel, serta memberikan kontribusi positif dalam mendorong praktik bisnis yang berkelanjutan.
Mapping and rebranding ornamental fish farming in Depok, West Java, contributions to the SDGs Harriz, Muhammad Alfathan; Setiyowati, Harlis; Akbariani, Nurhaliza Vania
Journal of Sustainable Tourism and Entrepreneurship Vol. 6 No. 2 (2025): January
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/joste.v6i2.2300

Abstract

Purpose: This study aims to identify and prioritize effective rebranding strategies for the ornamental fish farming industry in Depok, West Java, using the Analytic Hierarchy Process (AHP) to address challenges such as limited market access, environmental sustainability issues, and economic feasibility concerns while aligning with Sustainable Development Goals (SDGs). Research Methodology: The research employs a mixed-method approach, combining qualitative research through in-depth interviews with key stakeholders and quantitative analysis using the AHP to systematically evaluate and prioritize rebranding strategies based on multiple criteria. Results: The study identified market access as the most critical criterion, followed by environmental sustainability and economic feasibility, with digital marketing emerging as the most effective rebranding strategy, scoring 0.67, followed by sustainability certification (0.22) and community outreach programs (0.11). Limitations: The research is limited by a small sample size, potentially affecting result generalizability. The AHP methodology introduces possible subjective biases through pairwise comparisons. The study's regional specificity may constrain broader applicability. Furthermore, the emphasis on rebranding strategies may not encompass all potential solutions to industry challenges. Contribution: This research provides actionable insights for stakeholders in the ornamental fish farming industry to implement sustainable development strategies, aligning with SDGs 1, 8, and 14. It offers a systematic approach to decision-making through the application of AHP. Novelty: This study innovatively applies the AHP to rebranding strategies in ornamental fish farming, uniquely integrating sustainable development principles with quantitative decision-making. This approach offers a new paradigm for strategic planning in aquaculture, contrasting with traditional qualitative methods in the field.
Implementasi Random Forest dan SMOTE untuk Prediksi Risiko Putus Sekolah Dasar Menuju Indonesia Emas 2045 Muhammad Alfathan Harriz
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 3 No. 2 (2025): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v3i2.408

Abstract

This research investigates the implementation of Random Forest algorithms combined with Synthetic Minority Over-sampling Technique (SMOTE) to predict elementary school dropout rates in Indonesia, supporting the Indonesia Emas 2045 vision. A significant gap was identified in previous studies, which, despite utilizing artificial intelligence for dropout interventions, had not integrated temporal dimensions into data analysis. A temporal data-based classification model was developed using Indonesian Ministry of Education data from 2021-2023, incorporating lag features, delta calculations, and rolling statistics. Two models were implemented: one with SMOTE achieving 99% accuracy with perfect recall for high-risk regions, while the non-SMOTE model reached 100% accuracy. Temporal features were identified as crucial predictors, reflecting external fluctuations and annual changes impacting dropout decisions. This approach enables educational institutions to allocate resources more efficiently by prioritizing operational assistance for high-risk schools. The model's capacity to identify high-risk regions with 100% recall represents a strategic investment in strengthening Indonesia's human resource sustainability. To address the limitations of provincial aggregate data, expansion to include individual-level variables and model validation at district or school scales is recommended for future research.
The Role of Artificial Intelligence in Optimizing Scholarship Programs: Implications for Indonesia's Educational Development Prasetyo, Muhammad Teguh; Dazk, Erick; Akbariani, Nurhaliza Vania; Harriz, Muhammad Alfathan; Setiyowati, Harlis
Eduvest - Journal of Universal Studies Vol. 5 No. 4 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i4.44753

Abstract

This study explores the potential of Artificial Intelligence (AI) in optimizing scholarship allocation processes within Indonesia's higher education system. The research highlights the challenges faced in the traditional scholarship distribution methods, including inefficiencies and biases, which hinder equitable access to educational opportunities for underprivileged students. By employing a systematic literature review (SLR) using the PRISMA methodology, the study investigates various AI techniques, including K-Nearest Neighbors, Naive Bayes, Decision Trees, and Fuzzy Logic, to improve the accuracy and efficiency of scholarship selection. The findings demonstrate that AI-driven approaches can enhance transparency, fairness, and resource allocation, aligning with the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). The integration of AI can help optimize government programs like KIP Kuliah, ensuring that scholarships are awarded to the most deserving candidates. However, challenges such as improving data quality, model selection, and ensuring fairness and transparency in decision-making remain. Future research should focus on addressing these challenges, exploring more advanced AI techniques, and assessing the long-term impact of AI-driven scholarship allocation on educational outcomes and social mobility in Indonesia.
Latency Aware Edge Architectures for Industrial IoT: Design Patterns and Deterministic Networking Integration Harriz, Muhammad Alfathan
Digitus : Journal of Computer Science Applications Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v3i3.958

Abstract

This study explores the design patterns and latency budgets required for real time performance in edge based Industrial Internet of Things (IIoT) systems. As industrial applications increasingly demand ultra low latency for control loops and automation tasks, cloud computing architectures fall short in meeting strict timing requirements. The research investigates architectural configurations such as on premises edge computing, hybrid edge↔cloud frameworks, and 5G Multi access Edge Computing (MEC), all integrated with deterministic networking technologies like Time Sensitive Networking (TSN). The methodology includes modeling latency partitions across communication, computation, and execution layers, evaluating IIoT protocols such as OPC UA PubSub and MQTT Sparkplug B, and measuring metrics like end to end latency, jitter, and deadline miss percentages under realistic workloads. Results confirm that edge architectures, when combined with TSN and real-time operating environments, can achieve latency budgets as low as approximately 1 millisecond (ms) for servo loops and between 6–12 ms for machine vision tasks. These values highlight the feasibility of meeting industrial automation requirements. The conclusion underscores the importance of matching communication technologies wired TSN versus 5G URLLC according to environmental constraints and specific application requirements. It also emphasizes the role of hybrid architectures and standardized protocols in enabling scalable, interoperable, and deterministic IIoT systems. This work contributes a validated framework for deploying real time industrial systems capable of meeting the performance thresholds of Industry 4.0.
Co-Authors Akbariani, Nurhaliza Vania Dazk, Erick Handri Santoso Handri Santoso Harlis Setiyowati Indah Sulistyoningsih Lilik Hariyanto Luh Putu Puji Trisnawati Mayatopani, Hendra Nurhaliza Vania Akbariani Nurhaliza Vania Akbariani Nurhaliza Vania Akbariani Nurhaliza Vania Akbariani Sembiring, Rinawati