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All Journal Jurnal Sarjana Teknik Informatika Desimal: Jurnal Matematika BERDAYA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi MATHunesa: Jurnal Ilmiah Matematika Smart Society: Community Service and Empowerment Journal MDP Student Conference Indonesian Journal of Applied Mathematics Jurnal Pengabdian Masyarakat Terapan Jurnal Ilmiah Teknologi dan Komputer (JITTER)
Christyan Tamaro Nadeak
Data Science Study Program, Sumatra Institute Of Technology
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Deteksi Komunitas Pasar Saham IHSG dengan Metode Hybrid Jaringan Kompleks dan Algoritma Leiden Nadeak, Christyan Tamaro
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 3 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v13n3.p299-306

Abstract

The stock market is a complex system, with relationships between stocks that influence each other and form a dynamic network. In Indonesia, the Jakarta Composite Index (JCI) reflects the movement of the stock market as a whole. This study aims to detect the community structure of stocks in the JCI by sector using a hybrid approach that combines Random Matrix Theory (RMT), Complex Network (CN), and Leiden algorithm. The data used is the daily closing price of stocks in the JCI during the period January 2014 to January 2024. The methods applied include the formation of a correlation matrix between stocks, noise filtering using RMT, and community analysis using the Leiden algorithm. A multi-threshold correlation approach (0.7; 0.8; and 0.9) was used to evaluate the strength of the relationship between sectors. The results show that the combination of RMT, CN, and Leiden algorithm is effective in identifying stock communities with significant relationships. A higher correlation threshold results in a more stable community with a maximum modularity value of 0.72 at a threshold of 0.9. This approach makes an important contribution in understanding cross-sector interactions in the JCI stock market.
Community Detection of Singers in Spotify Rock Playlists Using Louvain Method Surya, Annisa Cahyani; Nadeak, Christyan Tamaro
JITTER: Jurnal Ilmiah Teknologi dan Komputer Vol. 7 No. 1 (2026): JITTER, Vol.7, No.1, April 2026
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

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

Abstract

Spotify is one of the leading music streaming platforms, allowing users to create playlists and select songs based on their preferences. Rock music has remained a prominent genre on Spotify, especially in Indonesia, where it holds historical and cultural significance and serves as a medium for social and political expression. This study investigates how user preferences shape the network structure among Indonesian rock artists. Using the Louvain community detection method, artists were grouped based on their co-occurrence in playlists to uncover community patterns within the genre. Data were collected by scraping playlists using the keyword “Rock Indonesia.” The optimal configuration was found with a k-core value of 3 and an edge weight threshold of 0.39, resulting in a modularity score of 0.4782. Three main communities were identified, differentiated by subgenre, active period, and record label.
Workshop Pembuatan Sistem Monitoring Jaringan Sederhana Menggunakan Python bagi Siswa SMKN 4 Bandar Lampung Wisnubroto, M. Syamsuddin; Yuliana, Yuliana; Rassiyanti, Linda; Lailani, Ade; Farid, Fajri; Nadeak, Christyan Tamaro; Nurjanah, Fitri; Suciati, Indah; Kurnia, Rian; Lestari, Yusni Puspha; Setiawan, Dewi Indra
BERDAYA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat Vol 8 No 2 (2026)
Publisher : LPMP Imperium

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36407/berdaya.v8i2.1810

Abstract

This community service program was conducted to strengthen vocational students' competencies in network monitoring using Python. The partner school's main challenge was that networking lessons were still centered on hardware-oriented tools such as Mikrotik and Cisco, while software-based monitoring skills had not been introduced systematically. The program took the form of a workshop at SMKN 4 Bandar Lampung on 24 September 2025 and combined short lectures, demonstrations, guided practice, and mini projects. The training module covered basic networking concepts, connectivity and server status, bandwidth and latency, Python fundamentals, and the use of requests, psutil, socket, subprocess, and pandas to build a simple network monitoring system. Evaluation was conducted descriptively using pre-tests, post-tests, and practical assessment. The results showed that the mean pre-test accuracy of 46% from 32 participants increased to 64% in the post-test completed, representing an 18 percentage-point gain. All students also completed the assigned Python-based monitoring practice successfully. The outputs included a training module, poster, short video, and press release to support sustainability and dissemination.
Klasifikasi Multikelas Varietas Kacang Kering Menggunakan Metode Hybrid SVM Berbasis DAG Nababan, Dinda; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14094

Abstract

This study analyzes the performance of three conventional SVM strategies, namely One-vs-One (OvO), One-vs-Rest (OvR), and Directed Acyclic Graph OvO (DAG-OvO), compared with the hybrid approach Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) in the context of multiclass classification using the Dry Bean Dataset. All models are evaluated based on accuracy and macro metrics to measure the consistency of predictions between classes. The results show that both conventional and hybrid methods achieve the same high level of accuracy, namely 0.92, with Precision, Recall, and F1-score Macro values ​​that were also identical between approaches. The main difference between the approaches lies in computational efficiency. OvO and DAG-OvO show the fastest training time, while DAG-RvR is the most efficient method in the inference stage. These findings confirm that the hybrid DAG-RvR structure can accelerate the prediction process without compromising accuracy, making it worthy of consideration for applications that require fast inference.
Klasifikasi Multikelas Support Vector Machine dengan Hibrida Directed Acyclic Graph One Vs One dan Rest Vs Rest pada Klasifikasi Tingkat Obesitas Naufal, Daffa Ahmad; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14097

Abstract

This research is focused on analyzing how well different multiclass Support Vector Machine (SVM) classification methods can predict obesity levels. It also presents a new hybrid Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) method as a better option. The study utilizes a dataset called the Obesity Risk Prediction Cleaned, which has information on seven different obesity categories. The methods being assessed include One-vs-One (OvO), One-vs-Rest (OvR), DAG-One-vs-One (DAG-OvO), and the new DAG-RvR method. For fine-tuning the parameters, GridSearchCV and the RBF kernel were used. The findings reveal that DAG-RvR achieves an accuracy of 0.91, which is similar to OvO and DAG-OvO, but it trains much quicker, taking just 0.3422 seconds. Even though its precision, recall, and F1-score are a bit lower than the pairwise methods, DAG-RvR still maintains reliable multiclass performance. In summary, this method strikes a good balance between achieving high accuracy and being efficient in computations.
Klasifikasi Varietas Beras Menggunakan Hybrid SVM Berbasis DAG–OVO dan RVR Leander, Marleta Cornelia; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14108

Abstract

This research proposes a hybrid Support Vector Machine (SVM) strategy for multiclass rice variety classification by combining Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) with K-Means clustering. Five rice varieties were analyzed using 16 morphological and texture features extracted from the Rice Image Dataset. Three conventional SVM methods—One-vs-One (OvO), One-vs-Rest (OvR), and DAG-OvO—were evaluated as baselines. Two hybrid schemes were then developed: DAG-RvR K-Means–OvO and DAG-RvR K-Means–K-Means. Experimental results show that all methods achieve high accuracy of approximately 99%, indicating strong feature separability among rice varieties. However, the proposed DAG-RvR K-Means–OvO provides the most efficient performance, achieving the fastest training time while maintaining competitive testing speed and the highest accuracy of 0.99040. The findings demonstrate that integrating K-Means–based class partitioning with pairwise SVM classification improves computational efficiency without reducing predictive performance, making the hybrid approach suitable for fast and accurate multiclass classification tasks.
Natural Resources Data Visualization Training Using Google Data Studio in Triharjo Village, Merbau Mataram District, South Lampung Regency Luluk Muthoharoh; Mika Alvionita S; Febri Dwi Irawati; Tirta Setiawan; Christyan Tamaro Nadeak
Smart Society Vol. 3 No. 2 (2023): Smart Society
Publisher : FOUNDAE (Foundation of Advanced Education)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/smartsociety.v3i2.287

Abstract

Visualization is becoming as the most frequent tool for examining and extracting information from datasets by novice and professional researchers alike. Many data processing applications help to present and report data. One of the digital tools that is quite widely used is Google Data Studio. This Community Service activity aims to provide data visualization training to Triharjo Village, one of the villages that still does not use digitalization to access village data online. Natural Resources Data Visualization Training Using Google Data Studio in Triharjo Village, Merbau Mataram District, South Lampung Regency has been successfully implemented and attended by 10 (ten) participants consisting of village officials. This activity is very necessary to facilitate the monitoring of agricultural products from Triharjo Village on the dashboard via the village website. The target in community service has also been achieved and serves to provide problem solving for problems that occur with partners, namely in the form of: 1. Can introduce the Tiharjo village community to the importance of digitizing performance dashboards. 2. Can teach how to use Google Data Studio tools which can be used to help the process of creating Dashboards. With this training, it is hoped that it can help manage natural resource data which will help village officials and village communities, teachers and students in providing information services related to data visualization.
Analisis Kinerja XGBoost dengan Penanganan Imbalanced Dataset Menggunakan SMOTE-Tomek pada Klasifikasi Penyakit Diabetes Rohmi Dyah Astuti; Christyan Tamaro Nadeak; Ade Lailani
Jurnal Sarjana Teknik Informatika Vol. 14 No. 2 (2026): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v14i2.32255

Abstract

Penyakit diabetes merupakan salah satu penyakit kronis yang mengalami peningkatan jumlah penderita secara signifikan dalam beberapa tahun terakhir dan memerlukan deteksi dini yang akurat. Ketepatan dalam proses klasifikasi penyakit diabetes sangat penting untuk membantu penanganan medis dan mengurangi risiko komplikasi pada pasien. Namun, permasalahan ketersediaan data yang tak seimbang pada data kesehatan seringkali menyebabkan model klasifikasi menjadi bias terhadap kelas mayoritas dimana jumlah penderita diabetes lebih sedikit dibanding jumlah bukan penderita diabetes. Penelitian ini bertujuan untuk menganalisis performa algoritma XGBoost dengan penerapan metode SMOTE-Tomek dalam menangani ketidakseimbangan data pada klasifikasi penyakit diabetes. Dataset yang digunakan terdiri dari 5288 data dengan 14 fitur merupakan faktor-faktor pendukung resiko terkena penyakit diabetes. Proses penelitian meliputi prapemrosesan data, pembagian data latih dan data uji, penanganan imbalanced dataset menggunakan SMOTE-Tomek, pelatihan model XGBoost dengan hyperparameter tuning menggunakan GridSearchCV, serta evaluasi model menggunakan metrik akurasi, precision, recall, F1-score, dan ROC-AUC. Dengan pembagian data latih dan data uji sebesar 80:20, hasil penelitian menunjukkan bahwa tanpa penanganan data tidak seimbang, model menghasilkan nilai precision sebesar 0,61, recall sebesar 0,30, dan F1-score sebesar 0,40 pada kelas minoritas. Setelah penerapan SMOTE-Tomek, nilai recall dan F1-score meningkat menjadi 0,45, meskipun precision menurun menjadi 0,45. Selain itu, nilai ROC-AUC meningkat dari 0,64 menjadi 0,70, yang menunjukkan peningkatan kemampuan model dalam membedakan kelas. Dengan demikian, kombinasi SMOTE-Tomek dan XGBoost terbukti mampu meningkatkan performa model dalam menangani dataset tidak seimbang, khususnya dalam mendeteksi kelas minoritas pada kasus klasifikasi penyakit diabetes.
Co-Authors Ade Lailani Andriana Putra, Randa Dyah Astuti, Rohmi Fajri Farid Farid, Fajri Fauzi Dzaki Arif, Sofyan Hilal Kurniawan, Danang Lailani, Ade Leander, Marleta Cornelia Lestari, Yusni Puspha Linda Rassiyanti Luluk Muthoharoh Mika Alvionita Sitinjak Nababan, Dinda Naufal, Daffa Ahmad Nurjanah, Fitri Putri, Lutfia Aisyah Rassiyanti, Linda Rian Kurnia, Rian Rohmi Dyah Astuti Ronal, Ronal Satria, Eggi Setiawan, Dewi Indra Siahaan, Arielva Simon Sihombing, Natanael Oktavianus Partahan Suciati, Indah Surya, Annisa Cahyani Tirta Setiawan Vidia, Vidia Wisnubroto, M. Syamsuddin Yuliana Yuliana Yustisia Sari, Rizki