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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Pengabdian Kepada Masyarakat Journal of Data Mining and Information Systems
Belferik, Ronald
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Analisis Average Waiting Time Penjadwalan CPU Menggunakan Algoritma Shortest Remaining First dan Algoritma Round Robin Belferik, Ronald; Banjarnahor, Evander
JDMIS: Journal of Data Mining and Information Systems Vol. 3 No. 1 (2025): February 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v3i1.4076

Abstract

In operating systems, process scheduling is a critical aspect to determine the order of process execution by the CPU. This research compares the average waiting time (AWT) of Shortest Remaining First (SRF) algorithm and Round Robin (RR) algorithm where the problem to be solved is CPU scheduling. The purpose of this research is to get an algorithm that has a short average waiting time. The test results obtained that the SRF algorithm has a very short average waiting time with a value of 29.85 ms compared to the RR algorithm which gets an AWT result of 65.6 ms.
Optimalisasi Pemasaran Digital dalam Meningkatkan Adopsi Perangkat Lunak Pemantau Produktivitas pada PT XYZ Ali Akbar Lubis; Belferik, Ronald; Ariwibowo, Suminar; Riche, Riche; Pratama, Yudhistira Adhitya
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 5 No 1 (2025): Mei 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v5i1.4195

Abstract

In 2019, the trend of work-from-home or remote working began to be adopted by several companies in Indonesia. Along with this trend, there was a growing need for software that could monitor the performance of remote employees, one of which was Aplikasi ABC. To increase the adoption of this software, a digital marketing strategy based on observation was implemented, where its effectiveness was analyzed using data obtained from various marketing channels. This activity focuses on several online marketing strategies applied to enhance engagement, conversion rates, and brand awareness. The initial goal of the digital marketing campaign was to achieve an engagement and click-through rate of 5% of total impressions. However, by the end of 2019, the achieved rate was only 2.35%, primarily due to factors such as low brand awareness, less appealing ad designs, and ineffective headlines that failed to capture the target audience’s attention. Additionally, the campaign aimed for a 10% monthly increase in impressions with a marketing budget of less than IDR 5,000,000 per month. The analysis results indicate that advertising costs, SEO optimization, and landing page content quality significantly influence engagement growth and user conversion rates. From this activity, it can be concluded that a more effective digital marketing strategy requires optimized audience targeting, improved ad design quality, and the utilization of more targeted marketing channels. Moreover, well-optimized websites and landing pages can enhance customer retention and support successful user acquisition. These findings can serve as a foundation for developing more effective digital marketing strategies in the future.
ANALISIS IMPLEMENTASI SUPPORT VECTOR MACHINE DAN RANDOM FOREST UNTUK PREDIKSI KATEGORI INDEKS KUALITAS UDARA JAKARTA Banjarnahor, Evander; Belferik, Ronald; Cendana, Wiputra; Abraham, Yohanes Adi Saputra
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 1 (2025): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i1.56477

Abstract

Kualitas udara yang buruk di Jakarta berdampak signifikan terhadap kesehatan masyarakat dan lingkungan. Oleh karena itu, diperlukan metode prediksi untuk membantu pengambilan kebijakan mitigasi polusi udara. Penelitian ini memprediksi kategori indeks kualitas udara dengan metode Support Vector Machine (SVM) dan Random Forest menggunakan data polutan (PM10, PM2.5, SO₂, CO, O₃, NO₂) dari Kaggle tahun 2021, meliputi PM10, PM2.5, SO2, CO, O3, dan NO2. Analisis korelasi menunjukkan bahwa PM10 dan PM2.5 memiliki hubungan yang sangat kuat (r = 0.96), menandakan keterkaitan erat dalam menentukan tingkat polusi udara. SVM dan Random Forest disimulasikan dengan berbagai rasio pembagian data latih dan uji (10:90, 15:85, 20:80, 25:75, dan 30:70), serta menggunakan stratified k-fold cross-validation untuk meningkatkan validitas hasil dan mengurangi potensi overfitting. Hasil evaluasi menunjukkan bahwa kedua model memberikan performa yang sangat baik dengan akurasi lebih dari 97% pada seluruh skenario pembagian data. Random Forest mencapai akurasi maksimum 100% pada rasio 15:85, sementara SVM mencatatkan akurasi tertinggi 98,9% pada rasio 25:75. Hasil cross-validation menunjukkan bahwa Random Forest mencapai akurasi 100% pada simulasi menggunakan 5-folds, dengan nilai presisi, recall, dan F1-score yang juga 100%. Di sisi lain SVM menunjukkan akurasi sedikit lebih rendah yaitu 97,30% namun lebih konsisten dengan standar deviasi 2,50%.
Addressing Class Imbalance in Stunting Classification Using SMOTE Enhanced Random Forest Belferik, Ronald; Sinaga, Frans Mikael; Ferawaty, Ferawaty; Manullang, Mangasa A.S.; Sinaga, Tetti
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15349

Abstract

Stunting is a chronic nutritional problem that poses serious long-term effects on children’s health, including impaired physical growth, delayed cognitive development, and reduced productivity in adulthood. Early and accurate detection of stunting is therefore essential to support effective public health interventions and targeted policy implementation. However, one of the central challenges in developing machine learning models for this purpose is the presence of class imbalance in health-related datasets. Such imbalance frequently leads to biased classifiers that perform well on majority classes but fail to identify minority categories, reducing the overall reliability of the system. To overcome this issue, the present study utilized the Synthetic Minority Oversampling Technique (SMOTE) to balance the distribution of classes in a dataset containing 110,000 records. A Random Forest algorithm was then employed as the base classifier, with hyperparameter optimization carried out using the Optuna framework to ensure robustness and generalizability. The experimental results demonstrate that the combined application of SMOTE and Optuna significantly improved classification performance, producing the highest Macro Area Under the Curve (AUC) of 0.9972. This outstanding score indicates the model’s superior ability to distinguish nutritional status categories across both majority and minority classes. The study concludes that addressing data imbalance through oversampling is a fundamental methodological step in constructing fair and effective machine learning systems for stunting detection, ultimately contributing to improved health outcomes and evidence-based policy design.
Co-Authors Abraham, Yohanes Adi Saputra Ali Akbar Lubis Ariwibowo, Suminar Banjarnahor, Evander Cendana, Wiputra Ferawaty, Ferawaty Manullang, Mangasa A.S. Riche Riche Sinaga, Frans Mikael Sinaga, Tetti Yudhistira Adhitya Pratama