Article Per Year (5 Year)
p-Index From 2021 - 2026
1.197
P-Index
This Author published in this journals
All Journal Jiko (Jurnal Informatika dan komputer) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Masyarakat Indonesia Jurnal Desain dan Analisis Teknologi Jurnal Ragam Pengabdian
Frans Mikael Sinaga
Universitas Pelita Harapan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Model Ensemble Stacking untuk Klasifikasi Big Data Stunting Berbasis XGBoost dan MLP Khairul Hawani Rambe; Frans Mikael Sinaga; Leni Anggraini Susanti; Moh. Erkamim
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 10 No. 1 (2026): Volume 10 Nomor 1 Januari 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v10i1.15903

Abstract

Klasifikasi status gizi balita berbasis data besar memerlukan pendekatan machine learning yang mampu menangani kompleksitas dan heterogenitas data secara akurat dan stabil. Populasi penelitian mencakup seluruh data rekam medis balita periode 2023–2024 yang diperoleh dari RS Mitra Medika Tanjung Mulia, dengan teknik pengambilan sampel menggunakan total sampling terhadap dataset yang tersedia. Sampel berupa data antropometri balita yang meliputi jenis kelamin, usia, berat badan, tinggi atau panjang badan, nilai Z-score, serta label status gizi. Metode yang digunakan adalah pendekatan kuantitatif berbasis machine learning dengan tahapan pra-pemrosesan, pembangunan model, dan evaluasi performa. Pra-pemrosesan mencakup pembersihan data, transformasi variabel kategorikal, normalisasi fitur numerik, serta pembagian data latih dan data uji dengan rasio 80:20. Model yang dikembangkan menggunakan pendekatan ensemble stacking dengan XGBoost sebagai base learner dan Multi-Layer Perceptron (MLP) sebagai meta learner. Evaluasi kinerja model dilakukan menggunakan confusion matrix, precision, recall, F1-score, dan akurasi. Hasil pengujian menunjukkan bahwa model stacking mencapai akurasi sebesar 99,64% dengan jumlah kesalahan prediksi yang sangat rendah serta nilai precision, recall, dan F1-score yang seimbang pada setiap kelas. Temuan ini menunjukkan bahwa integrasi algoritma boosting dan neural network mampu meningkatkan stabilitas dan kemampuan generalisasi model. Dengan demikian, pendekatan stacking XGBoost–MLP efektif dalam klasifikasi status gizi balita dan berpotensi diterapkan sebagai sistem pendukung keputusan deteksi dini masalah gizi berbasis big data.
Optimalisasi Pelayanan Pasien melalui Rancangan Sistem Antrean Digital pada UPT Puskesmas Bukit Lawang Frans Mikael Sinaga; Ferawaty Ferawaty; Katherin Yap; Randy Liminson; Manson Lie
Jurnal Ragam Pengabdian Vol. 3 No. 1 (2026): Januari-April. Synergy of Research and Community Service for Community Empowerm
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/dsbpgz74

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengoptimalkan kualitas pelayanan kesehatan melalui penerapan sistem antrean digital berbasis web di UPT Puskesmas Bukit Lawang. Permasalahan yang dihadapi meliputi penggunaan sistem antrean manual berbasis pencatatan buku dan pemanggilan langsung, yang berdampak pada kurang tertibnya antrean, tingginya waktu tunggu pasien, serta minimnya transparansi informasi layanan. Metode pelaksanaan kegiatan mencakup tahap observasi lapangan, analisis kebutuhan sistem, perancangan dan implementasi aplikasi, serta evaluasi melalui uji coba penggunaan dan penyebaran kuesioner kepada pengguna. Sistem yang dikembangkan terdiri dari fitur pengelolaan antrean oleh petugas melalui dashboard serta tampilan informasi antrean yang dapat diakses pasien secara langsung melalui layar display. Hasil kegiatan menunjukkan adanya peningkatan yang signifikan pada aspek keteraturan antrean, efisiensi waktu pelayanan, transparansi informasi, serta kemudahan dalam pelaksanaan tugas oleh petugas. Selain itu, waktu tunggu pasien mengalami penurunan yang cukup berarti, dan mayoritas pengguna memberikan respon positif terhadap penerapan sistem. Kendala yang muncul, seperti keterbatasan kemampuan teknologi dan gangguan jaringan, dapat diatasi melalui pelatihan dan pendampingan. Dengan demikian, penerapan sistem antrean digital berbasis web terbukti mampu meningkatkan mutu pelayanan kesehatan serta berpotensi untuk diterapkan pada fasilitas kesehatan lainnya
Upaya Peningkatan Kelola Keuangan di Kantin Dinas Pertanian Kabupaten Langkat Nurhayati; Arisman; Frans Mikael Sinaga; Ronald Belferik; Tuti Andriani; Irfan Nainggolan; Suhendra Simangunsong
Jurnal Masyarakat Indonesia (Jumas) Vol. 4 No. 03 (2025): Jurnal Masyarakat Indonesia (Jumas)
Publisher : Cattleya Darmaya Fortuna

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

Abstract

The canteen is one of the key facilities that supports the activities of employees as well as visitors in government institutions. However, a frequent issue encountered is financial management, which is still recorded manually in notebooks and not properly documented. This community service program was conducted at the Canteen of the Department of Agriculture, Langkat Regency, with the aim of providing guidance in manual financial recording, cash flow management, and monthly financial reporting. The methods applied in this program included observation, socialization, training, and evaluation. The results indicated an improvement in the manager’s ability to prepare daily and monthly financial reports, understand income and expenditure flows, and implement a recording system using Microsoft Excel. Through this program, it is expected that the canteen managers will be able to maintain transparency and accountability, thereby ensuring the sustainability of the canteen’s operations effectively.
Analisis Keamanan Data Pasien pada Sistem Informasi Manajemen Rumah Sakit (SIMRS) Maradona Jonas Simanullang; Mhd Adi Setiawan Aritonang; Frans Mikael Sinaga
Jurnal Desain Dan Analisis Teknologi Vol. 5 No. 1 (2026): Januari
Publisher : Aptikom Kepri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58520/jddat.v5i1.98

Abstract

Transformasi digital di sektor kesehatan mendorong rumah sakit untuk mengimplementasikan Sistem Informasi Manajemen Rumah Sakit (SIMRS) secara terintegrasi. SIMRS berperan penting dalam pengelolaan data pasien, termasuk rekam medis elektronik, administrasi pelayanan, serta pelaporan manajerial. Namun, meningkatnya ketergantungan terhadap sistem informasi juga meningkatkan risiko ancaman keamanan data pasien. Penelitian ini bertujuan untuk menganalisis keamanan data pasien pada SIMRS serta mengidentifikasi potensi risiko dan strategi mitigasi yang dapat diterapkan oleh rumah sakit. Metode penelitian yang digunakan adalah studi kasus dengan pendekatan deskriptif kualitatif, melalui observasi sistem, wawancara dengan tim teknologi informasi, dan analisis kebijakan keamanan informasi. Hasil penelitian menunjukkan bahwa ancaman keamanan data pasien meliputi akses tidak sah, kelemahan pengelolaan hak akses, serta kurang optimalnya penerapan standar keamanan informasi. Penelitian ini merekomendasikan penerapan kebijakan keamanan data yang komprehensif, peningkatan infrastruktur teknologi, serta penguatan sumber daya manusia untuk menjamin kerahasiaan, integritas, dan ketersediaan data pasien.
Model Data Mining untuk Penetapan Plafon Kredit dengan Algoritma C4.5 Frans Mikael Sinaga; Jefri Junifer Pangaribuan; Aulia Rizky Muhammad Hendrik Noor Asegaff; Wenripin Chandra; Riche Riche
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6656

Abstract

Manual credit limit determination in distributor companies is often subjective and inconsistent, increasing the risk of bad debts. This research aims to design an objective data mining model to support customer credit limit decisions at CV. XYZ. The method used is the Decision Tree with the C4.5 algorithm, applied to 66 historical records of customer payment data. Data analysis was performed by calculating Entropy and Information Gain values to build the decision tree, which was then validated using RapidMiner Studio software. The research successfully built a valid and consistent classification model. The "Piutang" (receivables/transaction volume per invoice) attribute was identified as the main determinant (root node), followed by the "Pembayaran" (payment history) attribute as a branch node. This model generates three interpretable decision rules, including the discovery of a risky pattern where high-volume customers with poor payment histories are associated with large credit limits. The proposed model can be implemented as a decision support tool to standardize credit policies, reduce subjectivity, and minimize the company's financial risk.
Digitalisasi Operasional UMKM Indekos: Implementasi Aplikasi Manajemen Penghuni, Pembayaran, dan Keluhan di Kota Medan Ali Akbar Lubis; Frans Mikael Sinaga; Riche Riche; Ade Sarah Huzaifah; Jefri Junifer Pangaribuan
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 4 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i4.6655

Abstract

MSME 'Indekos' (boarding houses) in Medan City are generally still managed manually, causing partners to face fundamental problems in service and administration. The billing process, reliant on face-to-face meetings, makes tracking payments difficult, while tenants are often late paying due to a lack of reminders. Furthermore, the absence of a documented complaint channel means owners often forget to follow up on issues. This community service (PKM) activity aimed to overcome these problems through operational digitalization. The priority problems addressed were (1) the digitalization of billing and payments to provide real-time arrears information, and (2) the provision of a documented complaint channel with status tracking. The PKM method involved Training and Mentoring for 3 'indekos' owners in Medan City. The science-and-technology (ipteks) solution, a web-based management application, was developed using the adaptive Extreme Programming (XP) methodology, allowing iterative improvements based on partner feedback. Implementation included business process mapping (Planning), application installation (Small Releases), and guided testing (Testing/Feedback). The result of this activity is a ready-to-use application (PHP/MySQL) delivered to partners, along with operational SOPs and training modules. The implementation successfully improved the owners' administrative efficiency and service transparency for tenants.
COMPARATIVE ANALYSIS OF SUPPORT VECTOR MACHINE AND RANDOM FOREST METHODS BASED ON RANDOMIZED SEARCH OPTIMIZATION IN HOAX NEWS CLASSIFICATION Vincent Lawrence; Frans Mikael Sinaga
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.11650

Abstract

In today’s digital era, the spread of hoax news has increasingly escalated alongside the ease of access to information through social media and online news portals. This phenomenon has caused negative impacts such as public confusion, social conflict, and a decline in public trust toward information accuracy. Therefore, an effective classification method is needed to accurately detect hoax news. This study aims to analyze and compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in classifying hoax news, by applying the Randomized Search optimization technique to enhance model performance. The dataset used in this research was obtained from Kaggle, titled Indonesia Fact and Hoax Political News, consisting of news titles and narratives as input attributes, and hoax or factual labels as outputs. The results show that the SVM algorithm without optimization achieved an accuracy of 83.92%, which increased to 84.28% after optimization using Randomized Search. Meanwhile, the Random Forest algorithm without optimization achieved an accuracy of 85.93%, which increased to 86.05% after optimization. Based on these findings, it can be concluded that the application of Randomized Search successfully improved the accuracy, sensitivity, and stability of the classification models, with the Random Forest algorithm providing the best performance in detecting hoax news in this study.
Co-Authors Ade Sarah Huzaifah Ali Akbar Lubis Arisman Aulia Rizky Muhammad Hendrik Noor Asegaff Ferawaty Irfan Nainggolan Jefri Junifer Pangaribuan Katherin Yap Khairul Hawani Rambe Leni Anggraini Susanti Manson Lie Maradona Jonas Simanullang Mhd Adi Setiawan Aritonang Moh. Erkamim Nurhayati Randy Liminson Riche Riche Ronald Belferik Suhendra Simangunsong Tuti Andriani Vincent Lawrence Wenripin Chandra