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All Journal Jurnal Sarjana Teknik Informatika Desimal: Jurnal Matematika 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 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 Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Pelabelan Antiajaib Berdasarkan Jarak pada Operasi Perkalian Tensor Graf Christyan Tamaro Nadeak
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 10 Issue 2 December 2022
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/euler.v10i2.16298

Abstract

Let  be a graph of order n. Let  be a bijection. For any vertex , the neighbor sum  is called the weight of the vertex  and is denoted by where N(v) is the open neighborhood of If  for any two distinct vertices  and then f is called a distance antimagic labelling. If the graph G admits such a labelling, then G is said to be a distance antimagic graph. This study gives a distance antimagic labelling for tensor product of two complete graph and sufficient condition so that the tensor product of a regular graph and complete graph is a distance antimagic graph.
Penerapan Algoritma Batchelor-Wilkins dalam Pengklasteran Graf Christyan Tamaro Nadeak
Indonesian Journal of Applied Mathematics Vol 3 No 1 (2023): Indonesian Journal of Applied Mathematics Vol. 3 No. 1 July Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v3i1.1259

Abstract

Batchelor-Wilkins Algorithm is a simple and heuristic clustering algorithm used when the number of classes is unknown. In this paper we will use Batchelor-Wilkins algorithm in graph clustering, specifically a Banana Tree Graph B(n,k), a graph obtained by connecting one leaf of each of n copies of a complete bipartite graph K_{1,k-1} to a single root vertex.
Distance Magic Labeling of Corona Product of Graphs Nadeak, Christyan Tamaro
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 6, No 1 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i1.38317

Abstract

Let G = (V, E) is a graph with order n, and f: V(G) → {1,2,...,n} is a bijection. For any vertex v ϵ V, the sum of f(u) is called the weight of vertex v, denoted by w(v), where N(v)  is the set of neighbors of vertex v. If the labeling f satisfies that there exists a constant k such that w(v)=k, for every vertex v in the graph G, then f is called a distance magic labeling for the graph G. If a graph G has a distance magic labeling, then G is called a distance magic graph. This paper presents a novel result that has not been extensively explored in previous research on the distance magic labeling for the corona product between several families of graphs, such as a complete, cycle, path, and star graph.Keywords: Distance magic labeling; Corona product; Complete graph; Cycle graph; Path graph; Star graph. AbstrakMisalkan G = (V, E) adalah graf berorde n, dan f: V(G) → {1,2,...,n}  merupakan suatu bijeksi. Untuk sebarang titik vϵ V, jumlahan dari f(u) merupakan bobot dari titik v dan dinotasikan dengan w(v), dengan N(v) merupakan himpunan tetangga dari titik v. Jika pelabelan f memenuhi terdapat suatu konstanta k sehingga w(v)=k, untuk setiap titik v yang terdapat pada graf G, maka f disebut sebagai pelabelan ajaib jarak bagi graf G. Jika suatu graf G memiliki pelabelan ajaib jarak, maka G disebut sebagai graf ajaib jarak. Paper ini memberikan hasil yang belum pernah dibahas sebelumnya, yaitu pelabelan ajaib jarak untuk operasi korona antara beberapa keluarga graf, seperti graf lengkap, graf siklus, graf lintasan, dan graf bintang.Kata Kunci: Pelabelan ajaib jarak; Operasi korona; Graf lengkap; Graf siklus; Graf lintasan; Graf bintang. 2020MSC: 
Distance Magic Labeling of Corona Product of Graphs Nadeak, Christyan Tamaro
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6 No. 1 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i1.38317

Abstract

Let G = (V, E) is a graph with order n, and f: V(G) → {1,2,...,n} is a bijection. For any vertex v ϵ V, the sum of f(u) is called the weight of vertex v, denoted by w(v), where N(v)  is the set of neighbors of vertex v. If the labeling f satisfies that there exists a constant k such that w(v)=k, for every vertex v in the graph G, then f is called a distance magic labeling for the graph G. If a graph G has a distance magic labeling, then G is called a distance magic graph. This paper presents a novel result that has not been extensively explored in previous research on the distance magic labeling for the corona product between several families of graphs, such as a complete, cycle, path, and star graph.Keywords: distance magic labeling; corona product; complete graph; cycle graph; path graph; star graph. AbstrakMisalkan G = (V, E) adalah graf berorde n, dan f: V(G) → {1,2,...,n}  merupakan suatu bijeksi. Untuk sebarang titik vϵ V, jumlahan dari f(u) merupakan bobot dari titik v dan dinotasikan dengan w(v), dengan N(v) merupakan himpunan tetangga dari titik v. Jika pelabelan f memenuhi terdapat suatu konstanta k sehingga w(v)=k, untuk setiap titik v yang terdapat pada graf G, maka f disebut sebagai pelabelan ajaib jarak bagi graf G. Jika suatu graf G memiliki pelabelan ajaib jarak, maka G disebut sebagai graf ajaib jarak. Paper ini memberikan hasil yang belum pernah dibahas sebelumnya, yaitu pelabelan ajaib jarak untuk operasi korona antara beberapa keluarga graf, seperti graf lengkap, graf siklus, graf lintasan, dan graf bintang.Kata Kunci: pelabelan ajaib jarak; operasi korona; graf lengkap; graf siklus; graf lintasan; graf bintang. 2020MSC: 
Prediksi Harga Penutupan Saham BMRI Menggunakan Metode Bidirectional Long Short-Term Memory Nadeak, Christyan Tamaro
Jurnal Ilmiah Matematika Vol. 12 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jim.v12i2.31310

Abstract

In recent years, Indonesia's capital market has grown significantly, with the number of investors rising from 3.8 million in 2020 to 12.3 million in January 2024. This study explores the application of the Bi-LSTM model to predict BMRI stock prices by systematically optimizing 75 models to obtain optimal hyperparameters. Unlike prior trial-and-error approaches, this research employs structured hyperparameter exploration using data splits of 70:30, 80:20, and 90:10 to evaluate model accuracy and stability. Results show excellent performance with a MAPE of 2.182% on BMRI’s historical closing prices from January 1, 2021, to July 31, 2024, using a 2-layer Bi-LSTM architecture, batch size 16, and 150 epochs. The findings confirm that an appropriate model can produce highly accurate predictions. This study provides insight into Bi-LSTM modeling in the banking sector, offering valuable references and strategic considerations for investors and stakeholders based on predictive results.
Application of the DAG-SVM for multi-class mobile phone price classification Sihombing, Natanael Oktavianus Partahan; Christyan Tamaro Nadeak; Linda Rassiyanti; Fajri Farid
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202529450

Abstract

This study investigated the application of multiclass Support Vector Machine (SVM) strategies for smartphone price range classification using the Mobile Price Classification dataset (N = 2,000). The aim was to assess whether the Directed Acyclic Graph SVM (DAG-SVM) could provide improvements in predictive performance or computational efficiency compared with the conventional One-vs-One (OvO) and One-vs-Rest (OvR) approaches. The dataset’s twenty features were standardized using Z-score normalization and split into training and testing sets with an 80:20 ratio. All models were implemented using a linear kernel and evaluated based on accuracy, macro-precision, macro-recall, macro-F1, and execution time. The results showed that both OvO and DAG-SVM achieved the highest performance, with an accuracy and macro-F1 score of 96.25%, while OvR performed substantially lower. Despite the theoretical efficiency of DAG-SVM, its Python-based sequential elimination process led to slower prediction time than OvO. This study contributed empirical evidence that execution time can diverge from theoretical expectations in practical implementations and demonstrated the importance of computational efficiency analysis when comparing multiclass SVM architectures for mobile price classification.
Enhancing multiclass SVM classification using a hybrid directed acyclic graph and rest-vs-rest strategy Nadeak, Christyan Tamaro; Farid, Fajri; Rassiyanti, Linda; Siahaan, Arielva Simon; Putri, Lutfia Aisyah
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202529469

Abstract

This study proposes a modified Directed Acyclic Graph Support Vector Machine (DAG-SVM) using a Rest-vs-Rest (RvR) strategy to address the multiclass classification problem in the Hepatitis C dataset from Kaggle, which contains four diagnostic categories with a highly imbalanced class distribution, with class sample sizes of 540, 24, 21, and 30, respectively. The aim of this study is to examine how hierarchical decision structures interact with extreme class imbalance in SVM-based multiclass classification. The method is implemented through three fixed hierarchical decision schemes {0,1} vs. {2,3}, {0,2} vs. {1,3}, and {0,3} vs. {1,2} which restructure the decision flow of conventional DAG-SVM. Experimental evaluation shows that although the proposed schemes achieve relatively high overall accuracy (0.91–0.93), the precision, recall, and F1-scores for minority classes remain extremely low. These findings offer a new empirical insight into how class imbalance propagates through the DAG hierarchy, leading to early elimination of minority classes, and highlight the need for imbalance-handling techniques such as resampling, cost-sensitive learning, or synthetic data generation. The contribution of this work lies in demonstrating the limitations of DAG-RvR under severe imbalance and providing a structured evaluation that can guide future improvements for more reliable multiclass recognition.
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.
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 Daffa Ahmad Naufal Dinda Nababan Fajri Farid Fajri Farid Farid, Fajri Linda Rassiyanti Linda Rassiyanti Luluk Muthoharoh Marleta Cornelia Leander Mika Alvionita Sitinjak Putri, Lutfia Aisyah Rassiyanti, Linda Rohmi Dyah Astuti Siahaan, Arielva Simon Sihombing, Natanael Oktavianus Partahan Surya, Annisa Cahyani Tirta Setiawan