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All Journal PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika Sains dan Teknologi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jambura Journal of Mathematics METIK JURNAL Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Griya Journal of Mathematics Education and Application Research in the Mathematical and Natural Sciences Research Review: Jurnal Ilmiah Multidisiplin Prosiding Seminar Nasional Pendidikan Matematika, Universitas Mulawarman Pattimura International Journal of Mathematics (PIJMath) Jurnal Riset Mahasiswa Matematika The Indonesian Journal of Computer Science Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Computational and Applied Mathematics
Isran K Hasan
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Implementasi Algoritma K-Means Untuk Mengelompokkan Mahasiswa Program Studi Pendidikan Matematika Berdasarkan Sumber Belajarnya Rizki, Nanda Arista; Kurniawan, Kurniawan; Hasan, Isran K.; Sampe, Nofia
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.584

Abstract

Students must be able to utilize learning resources properly to improve academic achievement. Students can be grouped based on the learning resources they use frequently. Grouping results are helpful for lecturers in designing, evaluating, and analyzing learning in the classroom. This research aimed to implement the K-Means algorithm to classify student learning resources and determine which learning resources determine which groups. The population of this research were students of the Mathematics Education study program at Mulawarman University who are still taking courses. At the same time, the sample were active students from classes 2019, 2020, 2021, and 2022 of the Mathematics Education Study Program at Universitas Mulawarman who were still taking courses and were willing to fill out the questionnaire, namely as many as 111 Students. The data analysis used was clustering analysis using the K-Means algorithm with the Elbow method. New dummy data was formed from learning resource data because it was multiple choice. Based on the results, three main groups were obtained according to the use of learning resources. The learning resources that determine the distribution of groups were electronic books and journals. The first group used electronic books and journals, while the third group did not use either. While the second group only used electronic books. The Silhouette value for this cluster model was 0.615. The classification was classified as good.
Penerapan Hybrid Metode ARFIMA-ANN Menggunakan Algoritma Backpropagation pada Peramalan Indeks Harga Saham Gabungan Buhungo, Rayhanul Jannah; Hasan, Isran K; Nurwan, Nurwan
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.28474

Abstract

The Composite Stock Price Index (IHSG) is a of the key indicator a country uses to assess its economic condition. The fluctuating movements of stock prices create uncertainly in the stock market, complicating decision-making for investors and government entities. Therefore, there is a need for a method that can forecast the Composite Stock Price Index to monitor such fluctuations. The objective of this study is to model the Composite Stock Price Index Utilizing a hybrid method and to assess the accuracy of this hybrid approach. The hybrid method employed is the Autoregressive Fractionally Integrated Moving Average (ARFIMA)-Artificial Neural Network (ANN). The results of this study show that the best ARFIMA model is ARFIMA (1,d,1) with a differencing parameter of dR/S = 0,362. The ANN model's optimal architecture obtained through the backpropagation algorithm is ANN (3,2,1). The accuracy of the hybrid ARFIMA-ANN model, measured by the Mean Absolute Percentange Error (MAPE), yielded of 1,0164%, lower than the MAPE value of 1,7326% for the standalone ARFIMA model. This suggests that the hybrid model improves forecasting accuracy and is the most efferctive model for predicting the IHSG. 
Comparison of Word2vec and CountVectorizer with Mutual Information in Support Vector Machine (SVM) for Public Sentiment Analysis Doholio, Nadya Pratiwi; Hasan, Isran K; Abdussamad, Siti Nurmardia
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6640

Abstract

Social media is widely used today. Along with the development of social media, it makes it not only a means of communication but also a means of exchanging opinions. One of the social media that is widely used to exchange opinions is X (Twitter). X is widely used to express opinions, particularly on controversial issues, such as the relocation of IKN. Therefore, sentiment analysis is needed to analyse public opinion regarding this national issue. SVM is widely used to classify sentiment based on several required categories, such as positive or negative. However, SVM will work even more effectively if the features used have good quality. Therefore, feature extraction and selection are necessary to enhance SVM classification accuracy. The selection of appropriate feature extraction is very important for classification. Therefore, this study aims to compare two feature extractions, namely Word2Vec and CountVectorizer by adding Mutual Information feature selection to SVM in classifying public sentiment from X. The results show that SVM with Word2Vec and CountVectorizer is more effective than SVM with Mutual Information feature selection. The results show that SVM with Word2Vec feature extraction and Mutual Information feature selection is more effective overall with 84% accuracy, 90% precision, 90% recall, and 90% f1-score, compared to SVM with CountVectorizer feature extraction and Mutual Information feature selection which has 80% accuracy, 83% precision, 92% recall, and 87% f1-score.
Evaluation of the Adaptive Fuzzy Neuro Inference System and Fuzzy Model Time Series Markov Chains in Forecasting Crude Oil Prices Hinelo, Ikrar Prasetyo; Nuha, Agusyarif Rezka; Hasan, Isran K; Nasib, Salmun K; Abdussamad, Siti Nurmardia
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6763

Abstract

The development of a country's economy is greatly influenced by global economic conditions, given the increasingly close links between countries through economic relations and international cooperation. One of the main factors in economic growth is international trade, particularly export and import activities. Crude oil is one of the most actively traded commodities. Given the highly volatile crude oil market, accurate price forecasts are crucial in economic and financial decision-making. This study compares the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Time Series Markov Chain (FTSMC) in forecasting the price of West Texas Intermediate (WTI) crude oil using time series data from 2020 to 2024 with saturated sampling technique. The implementation of both methods is carried out through Matlab Online and R-Studio software, with results showing that ANFIS has higher accuracy than FTSMC, as evidenced by the Mean Absolute Percentage Error (MAPE) value of 1,8010% for ANFIS and 3,7567% for FTSMC. Further analysis shows that ANFIS with a triangular membership function as well as significant lags at lag 1, lag 3, lag 4, and lag 7 is able to produce more accurate predictions and match the trend of actual data. Therefore, ANFIS is recommended as a more effective method in forecasting WTI crude oil prices, which can provide valuable insights for policy makers and industry stakeholders.
Optimization of LightGBM Model with Bayesian Optimization for Malware Detection Kasim, Afrianto Pratama; Nasib, Salmun K.; Hasan, Isran K.; Wungguli, Djihad; Yahya, Nisky Imansyah
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.722

Abstract

Cyberattacks through malware on Android devices continue to rise, making accurate detection crucial. This research optimizes the LightGBM model using Bayesian Optimization to enhance accuracy and efficiency in detecting Android malware. A feature selection mechanism based on Attention Mechanism is applied to select the most relevant features for classification. The dataset used comes from the Canadian Institute for Cybersecurity (CIC) and consists of 17,804 Android applications, with a balanced distribution between malware and normal applications. The dataset is split into ratios of 80%:20%, 75%:25%, and 70%:30%. Feature selection reduces the number of features from 9503 to 300, 500, and 1000. The LightGBM model is then optimized with Bayesian Optimization to fine-tune parameters such as learning rate, number of iterations, and maximum number of leaves. The model's performance is evaluated using accuracy, precision, and recall metrics. Experimental results show that the model achieves 96,99% accuracy, 97,30% precision, and 96,70% recall with an 80%:20% dataset split and 1000 features. The combination of Attention Mechanism and Bayesian Optimization effectively improves processing efficiency without compromising performance.
Perbandingan FTS Ruey Chyn Tsaur dan Saxena Easo Dalam Meramalkan Kunjungan Wisatawan Mancanegara Di Bali Ulopo, Asrul S; Djakaria, Ismail; Nashar, La Ode; Hasan, Isran K; Asriadi, Asriadi
Jurnal Riset Mahasiswa Matematika Vol 4, No 5 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i5.33304

Abstract

Provinsi Bali merupakan destinasi wisata utama di Indonesia yang setiap tahunnya menarik jutaan wisatawan mancanegara. Kunjungan wisatawan mancanegara di Provinsi Bali Januari sampai Juli 2024 menyambut kedatangan 3.538.899 wisatawan mancanegara, menunjukkan peningkatan signifikan sebesar 22,18% dibandingkan periode yang sama pada tahun sebelumnya. Peningkatan jumlah kunjungan tersebut menjadi indikator penting dalam pengembangan sektor pariwisata sekaligus penopang utama perekonomian daerah. Oleh karena itu, peramalan jumlah kunjungan wisatawan mancanegara di Bali menjadi langkah strategis untuk mendukung perencanaan dan pengambilan kebijakan yang efektif serta pengelolaan destinasi yang berkelanjutan. Penelitian ini bertujuan untuk membandingkan akurasi metode Fuzzy Time Series Ruey Chyn Tsaur dan Fuzzy Time Series Saxena Easo dalam meramalkan jumlah kunjungan wisatawan mancanegara di Bali. Data yang digunakan merupakan data sekunder dari Badan Pusat Statistik selama periode Januari 2005 hingga Desember 2024. Hasil penelitian menunjukkan bahwa FTS Ruey Chyn Tsaur memiliki tingkat akurasi yang lebih tinggi dengan nilai MAPE sebesar 5,544%, dibandingkan dengan FTS Saxena Easo yang menghasilkan MAPE sebesar 8,9256%. Kedua metode termasuk dalam kategori sangat akurat karena nilai MAPE yang diperoleh berada di bawah 10%. Evaluasi model terbaik menunjukkan bahwa pendekatan tersebut menghasilkan nilai MAPE sebesar 6,811%.
Perbandingan Seleksi Fitur Forward Selection dan Backward Elimination pada Algoritma Support Vector Machine Suharmin, Wandayana Nur'Amanah; Hasan, Isran K.; Yahya, Nisky Imansyah
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4755

Abstract

Support Vector Machine (SVM) is an effective and robust classification method, particularly when applied to high-dimensional data. However, high-dimensional data often contain irrelevant features that can lead to suboptimal SVM performance. Therefore, a feature selection process is necessary to optimize classification performance by eliminating irrelevant and redundant features from the original dataset. This research aims to compare the Forward Selection and Backward Elimination feature selection methods within the Support Vector Machine Algorithm for classification using the Poverty Depth Index data in Papua Province. The results indicated that applying the Support Vector Machine with Forward Selection feature selection achieved a classification accuracy of 93%, whereas Backward Elimination feature selection achieved a classification accuracy of 97%. Based on these classification accuracy results, it can be concluded that applying Support Vector Machine with Backward Elimination feature selection results in better performance than Forward Selection.
FORECASTING STOCK PRICES OF PT. BANK RAKYAT INDONESIA USING THE HYBRID ARIMA-BACKPROPAGATION NEURAL NETWORK METHOD Alaina, Silvana Rahmayanti; Hasan, Isran K.; Abdussamad, Siti Nurmardia
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page39-48

Abstract

PT. Bank Rakyat Indonesia (Persero) Tbk is classified as a blue-chip stock. Although investing in BRI shares has the potential to generate profits, stock price fluctuations can pose risks, making forecasting necessary. The ARIMA model is frequently used to predict such fluctuations, but struggles to capture non-linear patterns. ARIMA is combined with an Artificial Neural Network (ANN), specifically the Backpropagation Neural Network, to address this issue and improve forecasting accuracy. Although Backpropagation is weak in slow convergence, this can be overcome using the Conjugate Gradient Powell Beale (CGB) algorithm. The research results show that the closing stock price data of BRI from January 2023 to February 2024 produced an ARIMA (1,1,1)-Backpropagation [4-4-1] model with higher accuracy, achieving a MAPE of 2.516% and RMSE of 200.1592, Relative to the standalone ARIMA (1,1,1) model, which had a MAPE of 6.203% and RMSE of 421.5896.
Implementasi Algoritma K-Means Untuk Mengelompokkan Mahasiswa Program Studi Pendidikan Matematika Berdasarkan Sumber Belajarnya Rizki, Nanda Arista; Kurniawan, Kurniawan; Hasan, Isran K.; Sampe, Nofia
METIK JURNAL (AKREDITASI SINTA 3) Vol. 7 No. 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.584

Abstract

Students must be able to utilize learning resources properly to improve academic achievement. Students can be grouped based on the learning resources they use frequently. Grouping results are helpful for lecturers in designing, evaluating, and analyzing learning in the classroom. This research aimed to implement the K-Means algorithm to classify student learning resources and determine which learning resources determine which groups. The population of this research were students of the Mathematics Education study program at Mulawarman University who are still taking courses. At the same time, the sample were active students from classes 2019, 2020, 2021, and 2022 of the Mathematics Education Study Program at Universitas Mulawarman who were still taking courses and were willing to fill out the questionnaire, namely as many as 111 Students. The data analysis used was clustering analysis using the K-Means algorithm with the Elbow method. New dummy data was formed from learning resource data because it was multiple choice. Based on the results, three main groups were obtained according to the use of learning resources. The learning resources that determine the distribution of groups were electronic books and journals. The first group used electronic books and journals, while the third group did not use either. While the second group only used electronic books. The Silhouette value for this cluster model was 0.615. The classification was classified as good.
Density based spatial clustering of application with noise using flower pollination algorithm for leptospirosis clustering Karim, Finansiya S. Abd.; Rahmi, Emli; Abdussamad, Siti Nurmardia; Hasan, Isran K.; Yahya, Nisky Imansyah
PYTHAGORAS : Jurnal Program Studi Pendidikan Matematika Vol 14, No 1 (2025): PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika
Publisher : UNIVERSITAS RIAU KEPULAUAN, BATAM, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/pyth.v14i1.7505

Abstract

Leptospirosis is an important health problem in Indonesia, with most cases found in East Java and Central Java provinces. This study aims to identify the distribution pattern of leptospirosis in the two provinces using a clustering approach. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is used to cluster areas based on leptospirosis spread factors, but DBSCAN requires optimal parameter determination for accurate results. Therefore, this research implements Flower Pollination Algorithm (FPA) to optimize the epsilon (ϵ) and minimum points (MinPts) parameters in DBSCAN. This research uses secondary data obtained from data on the Number of Natural Disaster Events by Regency / City in East Java and Central Java Provinces in 2023 and data on Population Density by Regency / City in East Java and Central Java Provinces in 2023. The population in this study uses all observations, namely all people in the districts and cities in East Java and Central Java. The sampling technique is saturated sampling, that is, the entire population in the study is sampled. The clustering results using FPA-DBSCAN resulted in two main clusters, with 30 districts/municipalities detected as noise, 23 districts/municipalities belonging to cluster 0, and 20 districts/municipalities in cluster 1. The validation test using Silhouette Coefficient showed a value of 0.1892, indicating that the clustering is quite valid. The results of this clustering can serve as a strategic reference for local governments in optimizing disease surveillance and targeted health interventions.
Co-Authors Abubakar, Agung Sucipto Afandi W. Biga Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Alvitha Habibie Armayani Arsal Armayani Arsal Asriadi Asriadi Asriadi Asriadi Asriadi Awalia Emiro Buhungo, Rayhanul Jannah Demas Novaleda Abdul Karim Dewi Rahmawaty Isa Dewinto Burhan Djihad Wungguli Djuma, Clara Anggriani Doholio, Nadya Pratiwi Hanz Franklyn Bachruddin Wewengkang Hasan S. Panigoro HASIRU, LISA SYAHRIA Hinelo, Ikrar Prasetyo I Wayan Can Aryasandi Indrawati Lihawa Ismail Djakaria Ismail Saputra R. Harmain Ivan Novri Jefriyanto Ibrahim Karim, Finansiya S. Abd. Kasim, Afrianto Pratama Khairun Nisa Humolungo Kurniawan Kurniawan La Ode Nashar Lailany Yahya Misranti A. Samulu Mokodompit, Marcela Nadhilah, Farhah Nanda Arista Rizki Naufal Daffa Pahrun Nisky Imansyah Yahya NISKY IMANSYAH YAHYA Novianita Achmad Nur Anggraini T. Ali Nur Falaq Nur Miftah Muhammad Nur Siyam Djibu Nurul Afifah Nurwan Nurwan Nurwan, Nurwan Pakaya, Desya Neydi Putri Putri Lapitung Rahmi, Emli Ramdan Hayati Ramla Mohamad Resmawan Resmawan Saiful Pomahiya Salmun K. Nasib Sampe, Nofia Sigar, Tesya Siti Nurmardia Abdussamad Sri Ayu Nurdin Suci Tilome Suharmin, Wandayana Nur'Amanah Sumarno Ismail Suryahaty Aisyah Aulia Kaluku Thesalonica Graina Barung Trieke Nurfadilah Harun Ulopo, Asrul S Usman, Rahmat Setiawan Winni A. Pakaya