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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 Bulletin of Informatics and Data Science Servitium Smart Journal
Wilsen Grivin Mokodaser
Universitas Klabat, Sulawesi Utara, Indonesia
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.
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
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.
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.
Evaluasi Kinerja Algoritma Apriori dan FP-Growth untuk Association Rule Mining pada Data Transaksi Ritel Soewignyo, Fanny; Soewignyo, Tonny Irianto; Mokodaser, Wilsen Grivin; Silitonga, Argha Orion
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.v24i4.14952

Abstract

Ledakan data transaksi ritel yang terekam melalui sistem Point of Sale (POS) dan platform daring menuntut metode analisis yang efektif untuk menggali pola pembelian konsumen. Association Rule Mining merupakan pendekatan populer untuk menemukan keterkaitan antarproduk, dengan algoritma Apriori dan FP-Growth sebagai dua metode yang paling banyak digunakan. Penelitian ini bertujuan memberikan gambaran empiris mengenai efektivitas kedua algoritma tersebut pada data transaksi ritel yang nyata. Metode yang digunakan meliputi tahapan data understanding untuk mengenali struktur data, data cleaning untuk menghapus nilai kosong dan menyeragamkan format, serta data transformation menggunakan TransactionEncoder untuk mengubah data mentah menjadi format biner (one-hot encoded). Selanjutnya algoritma Apriori dan FP-Growth dijalankan dengan parameter yang sama untuk menghasilkan frequent itemsets dan aturan asosiasi. Evaluasi kinerja dilakukan dengan mengukur waktu pemrosesan, jumlah aturan yang dihasilkan, serta nilai support, confidence, dan lift tertinggi. Hasil penelitian menunjukkan bahwa kedua algoritma menghasilkan jumlah aturan yang sama (63 aturan) dengan support tertinggi 0,06, confidence tertinggi 0,51, dan lift tertinggi 3,29, tetapi waktu pemrosesan berbeda signifikan (Apriori 0,39 detik, FP-Growth 6,95 detik). Kesimpulannya, association rule mining efektif mengungkap pola pembelian konsumen, dan algoritma Apriori lebih efisien untuk dataset kecil hingga menengah, sedangkan FP-Growth lebih sesuai untuk dataset yang jauh lebih besar. Keywords - Association Rules, Apriori, FP-Growth, Frequent Itemset, Transaksi Ritel.
GreenPoin: Mobile Application with Reward Point System at Klabat University Adam, Stenly Ibrahim; Mokodaser, Wilsen Grivin; Wagiu, Wayne Gilbert
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 3 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i3.22163

Abstract

At Klabat University, the conventional method of deducting Sabbath points poses challenges for students and supervisors due to its inefficiency. To address this, the GREENPOIN smartphone application was developed as an innovative solution for managing Sabbath points. The application enables administrators to approve point redemptions, add supervisors, manage tasks, and monitor students' total Sabbath points. Students earn points by completing environmental cleaning tasks assigned by supervisors. The system was designed using prototype models and use case diagrams to evaluate user requirements. Key features include Mission Management and Validation for supervisors; Supervisor Management, Point Management, and Point Redemption Approval for administrators; and Login, Registration, Point Viewing, Mission Viewing, Task Submission, Point Redemption, History, and Point Reset for students. GREENPOIN has proven to enhance the efficiency of Sabbath point management, accelerate task validation, and streamline mission oversight. The application increases process transparency and encourages student participation in campus cleanliness initiatives. Future enhancements will include push notifications, an iOS version, mission-specific comments, and API integrations, such as Google Maps, to further improve functionality.
FORECASTING HEALTH INSURANCE PAYER INCOME: A COMPARATIVE ANALYSIS OF DECISION TREE AND SVR ALGORITHMS Wilsen Grivin Mokodaser; Tonny Irianto Soewignyo; George Morris William Tangka; Fanny Soewignyo
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.
Co-Authors Argha Orion Silitonga Argha Orion Silitonga Cherry Lumingkewas Erienika Meiling Lompoliu Fanny Soewignyo George M W Tangka George Morris William Tangka Hartiny Koapaha Joe Yuan Mambu Koapaha, Hartiny Longkutoy, Desmond Marchel Timothy Tombeng Monica Dwijayanti Raissa Camilla Maringka Regi Fernando Najoan Samidi Samidi Silitonga, Argha Orion Stenly Ibrahim Adam Stenly Ibrahim Adam Stenly Richard Pungus Tangka, George Toetik Wulyatiningsih Tonny Irianto Soewignyo Tonny Irianto Soewignyo Triyono, Gandung Wagiu, Wayne Gilbert