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All Journal Techno.Com: Jurnal Teknologi Informasi CogITo Smart Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Riset Informatika Idealis : Indonesia Journal Information System International Journal of Engineering, Science and Information Technology Servitium Smart Journal
Wilsen Grivin Mokodaser
Universitas Budi Luhur
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Implementasi Metode Indexing dan Penggunaan Subquery untuk Optimalisasi Database Rawat Jalan Rumah Sakit Menggunakan Mysql Wilsen Grivin Mokodaser; Monica Dwijayanti; Samidi Samidi
CogITo Smart Journal Vol. 8 No. 2 (2022): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v8i2.415.335-345

Abstract

The need for outpatient hospital services that can be accessed quickly by the community is a very important aspect. By looking at the downturn caused by the Covid-19 pandemic that has hit the world since 2019 and has an impact on various economic aspects including hospital services, digitizing the hospital system must be implemented to be a solution in providing fast services for patients who come for treatment. The information system is not without problems, as the amount of data increases so that the selection of the right database including the use of appropriate queries can help provide accurate and fast output. indexing method can be applied to tables with a large number of databases. the use of subqueries as with previous research shows an increase in data access performance.
PEMODELAN SISTEM PENDUKUNG KEPUTUSAN UNTUK PEMILIHAN JENIS KELAS JAMINAN LAYANAN BPJS KESEHATAN DENGAN METODE ANALITYCAL HIERARCHI PROCESS Mokodaser, Wilsen Grivin; Triyono, Gandung
IDEALIS : InDonEsiA journaL Information System Vol 6 No 2 (2023): Jurnal IDEALIS Juli 2023
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v6i2.3029

Abstract

Pada tahun 2021 menunjukkan hanya 68, 38% penduduk Indonesia yang memiliki jaminan kesehatan. Kelompok jaminan Kesehatan menurut penanggung pembiayaan adalah BPJS Kesehatan dengan pembiayaan dari pemerintah sebesar 38,46%, BPJS Kesehatan dengan pembayaran iuran perorangan sebesar 22,03% dan sebanyak 8,45% masyarakat indonesia memiliki Jaminan Kesehatan daerah (Jamkesda), jaminan kesehatan lainnya menggunakan jasa perusahaan atau kantor tempat masyarakat bekerja dimana presentasenya sebanyak 2,93%, dan untuk pembiayaan perorangan atau mandiri 0,76%. Metode Analitycal Hierarchi Process(AHP) dapat digunakan untuk menyelesaikan permasalahan dalam pemilihan kelas layanan kesehatan karena dapat menyederhanakan proses hierarki yang rumit dalam pengambilan keputusan. Hasil penerapan metode ini terlebih dahulu harus dilakukan pengujian Concistency Ratio(CR) untuk melihat apakah nilainya sesuai dengan aturan yang berlaku dimana Concistency Ratio(CR) > 10% atau 0,1. Nilai Index Ratio(IR) yang digunakan pada penelitian ini adalah 0.58 dimana jumlah kriterianya adalah 3 kriteria. Dengan penerapan metode Analitycal Hierarchi Process(AHP) pada kasus pemilihan kelas jaminan kesehatan ini didapkan nilai total rata-rata dari dari masing-masing kelas layanan kesehatan yaitu K1 = 0.562151, K2 = 0.2888765 dan K3 = 0.149084.
Information Technology Governance Analysis Using COBIT 2019 Framework at Bank Mandiri Girian Bitung Branch Wulyatiningsih, Toetik; Mokodaser, Wilsen Grivin; Mambu, Joe Yuan
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.642

Abstract

The advancement of information technology (IT) has become essential for organizations, including Bank Mandiri, where it underpins critical business functions. This study examines the implementation of IT governance at Bank Mandiri’s Girian branch using the COBIT 2019 framework, a comprehensive tool for managing IT processes effectively. Through a qualitative case study approach and interviews with key stakeholders, the study analyzes 40 IT processes across 11 design factors, with each factor scored between 75 and 100 to prioritize their importance. High-priority processes, such as Managed Solutions Identification and Build (BAI03), Managed Requirements Definition (BAI02), Managed IT Changes (BAI06), and Managed Projects (BAI11), are identified as critical to operational stability, customer satisfaction, and strategic alignment. These objectives play a fundamental role in resource allocation, supporting seamless IT operations and enhancing customer service. Processes with lower scores are deprioritized, allowing strategic focus on high-impact areas. This prioritization framework helps ensure efficient resource use, aligns IT governance with organizational goals, and reinforces the branch’s commitment to achieving reliable, customer-focused IT management. The study underscores the indispensable role of IT in supporting Bank Mandiri’s operations, where any IT disruption could significantly impact business continuity and customer satisfaction.
Integrasi XGBoost dan Visualisasi Gradio untuk Memprediksi Pendapatan Pembayar Asuransi: Studi Kasus Rumah Sakit Swasta di Manado Lumingkewas, Cherry; Mokodaser, Wilsen Grivin
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12633

Abstract

Prediksi pendapatan yang akurat sangat penting untuk menjaga keberlanjutan finansial, mengoptimalkan alokasi sumber daya, dan membantu perencanaan strategis rumah sakit. Untuk memprediksi pendapatan bulan Agustus, data historis pendapatan digunakan dari Januari hingga Juli 2022. Pra-pemrosesan data termasuk menangani nilai kosong atau nilai yang tidak ada, memilih fitur, dan membagi data menjadi set pelatihan dan pengujian. Metode utama untuk pelatihan model adalah algoritma XGBoost. Hasil evaluasi model diperoleh Mean Squared Error (MSE) dengan hasil 3.239, dan R-squared (R2) 0.99884, Metrik-metrik ini menunjukkan hasil prediksi yang sangat baik. Ditemukan bahwa data bulan Juni dan Juli memberikan kontribusi terbesar. Sistem dibangun dalam bentuk dashboard interaktif berbasis Gradio untuk meningkatkan aksesibilitas dan pemanfaatan hasil model. Oleh karena itu, manajemen rumah sakit dapat menggunakan solusi ini untuk membuat keputusan yang lebih tepat dan berbasis data. Secara keseluruhan, penelitian ini menunjukkan bahwa penggabungan pembelajaran mesin dan visualisasi interaktif dapat sangat bermanfaat dalam manajemen keuangan rumah sakit. Dengan kata lain, integrasi machine learning ke dalam sistem operasional membuka jalan bagi pengambilan keputusan yang lebih berbasis data (data-driven decision making) dan adaptif terhadap dinamika pasar.  Kata kunci – Xgboost, Pendapatan, Prediksi, Gradio
FORECASTING HEALTH INSURANCE PAYER INCOME: A COMPARATIVE ANALYSIS OF DECISION TREE AND SVR ALGORITHMS Mokodaser, Wilsen Grivin; Soewignyo, Tonny Irianto; Tangka, George Morris William; Soewignyo, Fanny
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2466.493 KB) | DOI: 10.34288/jri.v7i3.369

Abstract

An insurance company is a type of non-bank financial institution that protects clients from risks and collects premiums over a certain period, these facts provide an overview of the insurance business and highlight its role in the economy, this study evaluated the performance difference between the Decision Tree Regressor and Support Vector Regression (SVR) in predicting insurance payer income. The Decision Tree model demonstrated strong predictive accuracy, achieving a Mean Absolute Error (MAE) of approximately 57 million and an R-squared (R²) value of 0.896, meaning it could explain around 89.6% of the variance in the data. Additionally, the model maintained high consistency, as evidenced by 5-fold cross-validation scores ranging from 0.908 to 0.967, indicating strong generalization and low risk of overfitting. In contrast, the SVR model significantly underperformed. It recorded a much higher MAE of over 237 million and a large Mean Squared Error (MSE), reflecting substantial deviations from the actual values. Its R² score of -0.299 suggests that SVR performed worse than a naive mean predictor, failing to identify meaningful patterns. This poor performance was consistent across all cross-validation folds, which also produced negative R² scores. The SVR model’s inadequacy is likely due to the large scale of the income data and the lack of proper preprocessing, such as normalization, or parameter tuning. Overall, these findings clearly demonstrate that the Decision Tree Regressor is a more suitable, accurate, and stable model for predicting insurance payer income.
Aligning Information Technology Governance with Business Goals Using the COBIT 2019 Framework: A Case Study of a Innovation Consultancy Firm Mokodaser, Wilsen Grivin; Mambu, Joe Yuan; Koapaha, Hartiny; Lompoliu, Erienika
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.799.548-560

Abstract

Technological advancements have ushered the world into a new era, particularly in the realm of information. In this context, information technology is considered a crucial tool to support and enhance corporate management, enabling companies to compete in the market. One common approach to managing information technology is by using IT governance frameworks such as COBIT (Control Objectives for Information and Related Technology) 2019. To date, there has been no research or evaluation conducted on the IT performance of Meet Ventures, Pte. Ltd., leaving the maturity level of information technology implementation in the company unclear. This project aims to explore the use of information systems and information technology at Meet Ventures, Pte. Ltd. in managing their operations and data. The project's approach involves a literature study on COBIT 2019 design factors. Based on the application of COBIT 2019, the prioritized objectives identified are BAI03 – Manage Solutions Identification and Build, BAI06 – Manage IT Changes, and MEA01 – Monitor, Evaluate, Assess Performance and Conformance. The assessment of various design factors in COBIT 2019 indicates that Meet Ventures, Pte. Ltd. has a strong focus on innovation, differentiation, and customer service. They also demonstrate a commitment to legal compliance and external regulations, a customer service culture, and effective risk management.
Model Random Forest Data Historis Multivariat Untuk Prediksi Pendapatan Asuransi Mokodaser, Wilsen Grivin; Koapaha, Hartiny; Adam, Stenly Ibrahim
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 2 (2025): Jurnal IDEALIS Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i2.3512

Abstract

Perusahaan asuransi adalah perusahaan keuangan non-bank yang melindungi nasabah dari risiko dan mengumpulkan uang dari premi nasabah selama periode tertentu, sesuai dengan ketentuan polis. Karena perusahaan asuransi telah lama terlibat dalam perekonomian negara, masyarakat tidak begitu ragu akan layanan yang mereka tawarkan. Disebabkan oleh ketidakpastian yang terkait dengan hal-hal seperti kesehatan, pendidikan, harta-benda, dan kematian, kesadaran masyarakat tentang pentingnya asuransi terus meningkat. Asuransi menjadi alat penting bagi masyarakat untuk mengantisipasi risiko atau kerugian di masa depan. model Random Forest diterapkan untuk memprediksi pendapatan asuransi bulan berikutnya berdasarkan data historis multivariat dari bulan Januari hingga Juli/Agustus. Hasil evaluasi menunjukkan bahwa model memiliki performa yang cukup baik dalam menangkap pola pendapatan, dengan skor evaluasi Mean Absolute Error (MAE) sebesar ±25.139.426 menunjukkan bahwa rata-rata kesalahan prediksi hanya sekitar 25 juta rupiah, angka yang masih tergolong wajar jika dibandingkan dengan skala pendapatan keseluruhan. Mean Squared Error (MSE) sebesar 2.9815 × 10¹⁵ mencerminkan adanya beberapa error besar, meskipun hal ini wajar mengingat skala data dan keberadaan outlier yang sulit dihindari. R² Score sebesar 0.85 menandakan bahwa 85% variabilitas pendapatan dapat dijelaskan oleh model dari data historis, yang menunjukkan performa prediksi yang sangat baik. Kontribusi ilmiah dari penelitian ini adalah penerapan pendekatan regresi non-linear berbasis Random Forest untuk melakukan peramalan pendapatan asuransi menggunakan data multivariat historis bulanan, yang jarang dibahas secara mendalam dalam konteks industri asuransi. Pendekatan ini tidak hanya menyoroti efektivitas Random Forest dalam menangkap pola musiman dan hubungan non-linier antar variabel waktu, tetapi memberikan landasan eksplorasi metode machine learning lanjutan dalam analisis data asuransi.
IMPLEMENTASI SISTEM REKOMENDASI PRODUK E-COMMERCE MENGGUNAKAN CONTENT-BASED FILTERING BERBASIS COSINE SIMILARITY Adam, Stenly Ibrahim; Mokodaser, Wilsen Grivin
Simtek : jurnal sistem informasi dan teknik komputer Vol. 10 No. 2 (2025): Oktober 2025
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v10i2.1665

Abstract

Pesatnya perkembangan e-commerce menghadirkan tantangan berupa banyaknya pilihan produk yang dapat menimbulkan information overload bagi konsumen. Untuk mengatasi permasalahan tersebut, penelitian ini mengembangkan sistem rekomendasi produk berbasis Content-Based Filtering dengan Cosine Similarity. Metode ini memanfaatkan kombinasi fitur teks (judul dan deskripsi produk) yang direpresentasikan dengan TF-IDF, serta fitur numerik (harga, rating, dan jumlah rating) yang dinormalisasi menggunakan StandardScaler. Selanjutnya, seluruh fitur digabungkan dan dihitung tingkat kesamaannya dengan cosine similarity untuk menghasilkan rekomendasi produk yang relevan. Hasil penelitian menunjukkan bahwa pendekatan ini mampu memberikan rekomendasi yang logis, di mana produk dengan spesifikasi serupa ditampilkan secara berurutan berdasarkan tingkat kesamaan. Analisis tambahan juga memperlihatkan bahwa mayoritas produk memiliki rating tinggi meskipun harga bervariasi, menunjukkan harga bukan satu-satunya indikator kualitas. Dengan demikian, sistem ini terbukti efektif membantu konsumen dalam menemukan produk sesuai preferensi sekaligus memberikan insight bagi pelaku e-commerce.
Penguatan Tata Kelola Pemilu Digital melalui Evaluasi Implementasi SIREKAP di KPU Provinsi Sulawesi Utara Tangka, George; Longkutoy, Desmond; Mokodaser, Wilsen Grivin
Servitium Smart Journal Vol 4 No 1 (2025): Servitium Smart Journal
Publisher : Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/servitium.v4i1.43

Abstract

The 2024 simultaneous elections relied on the Vote Recapitulation Information System (SIREKAP) to enhance transparency, but its implementation in North Sulawesi faced technical and non-technical challenges. This community service activity evaluated SIREKAP's implementation using the CIPP model (Context, Input, Process, Product) through in-depth interviews and FGDs with 30 election organizers. Key findings revealed three critical issues: (1) uneven internet infrastructure causing 40% of remote polling stations to fail in data uploads, (2) low digital literacy among 65% of polling officers (KPPS), and (3) only 85% accuracy of the OCR feature in reading C1 forms. Recommendations include simulation-based training, offline mode development, and open-source code audits. The study underscores the need for a holistic approach to digital election transformation, integrating technical, human resource, and infrastructure aspects
Association Pattern Analysis of Global Company Market Capitalization Using the FP-Growth Algorithm with Load Balancing Constraint Adam, Stenly Ibrahim; Pungus, Stenly Richard; Mokodaser, Wilsen Grivin
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14885

Abstract

This research focuses on analyzing the global company market capitalization dataset using the FP-Growth algorithm combined with a load-balancing constraint approach. The main objective is to identify association patterns among different market capitalization categories Small, Medium, Large, Mega, and Ultra to understand their distribution and interrelationships. The study begins with data preprocessing, cleaning, and categorization of companies based on their market values. The FP-Growth algorithm is applied with a minimum support threshold of 0.02, and a load balancing constraint is introduced by filtering rules with support ≥ 0.05 and lift > 1, ensuring balanced and significant association patterns. The analysis results show that the most dominant categories are Medium and Small, representing the majority of companies worldwide, while Large, Mega, and Ultra categories are relatively rare. The strongest rule indicates that countries with “Large” companies are very likely to also have “Small” and “Medium” companies. Evaluation metrics show an average lift of 1.171 and an average confidence of 1.000, confirming strong and reliable associations. Overall, this study provides insights into global market capitalization patterns and demonstrates the effectiveness of FP-Growth with constraints in revealing meaningful, balanced relationships within large-scale business data.   Keywords – FP-Growth, Load Balancing Constraint, Market Capitalization, Association.
Co-Authors Cherry Lumingkewas Erienika Meiling Lompoliu Fanny Soewignyo George Morris William Tangka Joe Yuan Mambu Koapaha, Hartiny Longkutoy, Desmond Monica Dwijayanti Samidi Samidi Silitonga, Argha Orion Stenly Ibrahim Adam Stenly Richard Pungus Tangka, George Toetik Wulyatiningsih Tonny Irianto Soewignyo Triyono, Gandung Wagiu, Wayne Gilbert