Article Per Year (5 Year)
p-Index From 2021 - 2026
5.617
P-Index
This Author published in this journals
All Journal Jurnal Statistika Universitas Muhammadiyah Semarang PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Applied Statistics 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 Research in the Mathematical and Natural Sciences 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
Universitas Negeri Gorontalo
Agriculture, Biological Sciences & Forestry 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 Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
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.
Perbandingan Model ARIMA-RBF dan ARIMA-GARCH dalam Peramalan Time Series Inflasi Provinsi Gorontalo Emiro, Awalia; Hasan, Isran K; Achmad, Novianita
Research in the Mathematical and Natural Sciences Vol. 2 No. 1 (2023): November 2022-April 2023
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v2i1.76

Abstract

A quantitative method that is observed sequentially from time to time is a time series. In the real word, problems often occur where one method is not able to solve the problem. This research used linear and nonlinear methods by combining the ARIMA-RBF anda ARIMA-GARCH models in forecasting, and then the two models were compared based on the MAPE value. This research used monthly data on inflation for housing, water, electricity, and other fuels from 2008 to 2020. The forecast results from the ARIMA-RBF model obtained the MAPE value of 7.5%, and the ARIMA-GARCH model obtained the MAPE value of 11.8%. thus, the best model for predicting inflation in this research was the ARIMA RBF model.
Model Antrian Pelayanan Terhadap Nasabah Bank BRI Menggunakan Petri Net dan Aljabar Max Plus Nurdin, Sri Ayu; Yahya, Lailany; Hasan, Isran K; Nurwan, Nurwan
Research in the Mathematical and Natural Sciences Vol. 2 No. 2 (2023): May-October 2023
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v2i2.106

Abstract

Petri net is one model representing transitions and places connected by arrows. Max Plus Algebra is an algebraic structure in which all sets of real numbers  are equipped with max (maximum) and (addition). This research created a Petri net model of the customer service system for Bank BRI and a Max Plus Algebra model related to time to minimize service time at Bank BRI. The result is periodic time or characteristic values and vector characteristics where the values and are . The value of this vector's characteristics becomes a periodic time, which only takes 2 days 3 hours during working hours to disburse money after the client's arrival.
Peramalan Inflasi di Provinsi Gorontalo Menggunakan Metode General Regression Neural Network (GRNN) Hasan, Isran K; Achmad, Novianita; Lapitung, Putri
Research in the Mathematical and Natural Sciences Vol. 3 No. 1 (2024): November 2023-April 2024
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v3i1.150

Abstract

Forecasting the inflation rate is important because the results obtained are used as an indicator that can influence the policies that will be made later. One policy that uses the results of this forecasting as one of the things that can influence it is economic policy and monetary policy. In this study, the method used is the general regression neural network (GRNN). This forecast is applied to inflation data in Gorontalo Province from January 2008 to April 2023, with the conclusion that it produces an inflation forecast for May – December 2023 with a MAPE value of 3.24% or an accuracy rate of 96.76%.
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. 
Perbandingan Metode K-Means dan K-Medoids Dengan Validitas Davies-Bouldin Indeks, Dunn Indeks dan Indeks Connectivity Pada Pengelompokkan Masyarakat Penerima Bantuan Langsung Tunai Kilo, Nur Ain; Katili, Muhammad Rifai; Hasan, Isran K
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.190

Abstract

This study discusses the comparison between the K-Means and K-Medoids methods in grouping direct cash assistance (BLT) recipients, with an assessment using three validity indices: Davies-Bouldin Index (DBI), Dunn Index, and Connectivity Index. The main objective of this study is to determine the most effective clustering method for grouping BLT recipient data by considering the quality of the resulting clustering. In the experiment, the K-Means method with three clusters produced, namely: Cluster 1 with 10 family head members, Cluster 2 with 101 family head members, and Cluster 3 with 118 family head members. In contrast, the K-Medoids method also with three clusters, namely: Cluster 1 with 67 family head members, Cluster 2 with 59 family head members, and Cluster 3 with 103 family head members. Based on the evaluation using the Davies-Bouldin Index and Connectivity Index, the K-Means method showed better performance than K-Medoids. The DBI value for the K-Means method is 1,307, while the Connectivity Index value is 40,079, which shows that the K-Means clustering results are more effective in producing separate and quality clusters in the context of grouping BLT recipient communities.
Pemilihan Metode Optimal Untuk Prediksi Angka Kemiskinan Di Provinsi Gorontalo: Perbandingan Double Exponential Smoothing dan Bayesian Structural Time Series wolah, Meitasya; Nasib, Salmun K.; Arsal, Armayani; Hasan, Isran K.; Asriadi; Abdussamad, Siti Nurmardia
Research in the Mathematical and Natural Sciences Vol. 4 No. 1 (2025): November 2024-April 2025
Publisher : Scimadly Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55657/rmns.v4i1.202

Abstract

Kajian ini mengevaluasi angka kemiskinan di Indonesia yang masih tinggi dengan fokus pada Provinsi Gorontalo yang menjadi urutan kelima sebagai provinsi termiskin di Indoneisa. Meskipun angka kemiskinan ekstrem nasional menurun menjadi 1,12% pada Maret 2023, Gorontalo mencatat masih 183,71 ribu penduduk miskin dengan garis kemiskinan per kapita sebesar Rp 442.194. Tujuan penelitian ini untuk membandingkan dua teknik peramalan, yaitu Bayesian Structural Time Series (BSTS) dan Double Exponential Smoothing (DES) untuk menilai efektivitas masing-masing metode dalam memprediksi angka kemiskinan di Provinsi Gorontalo. Hasil analisis menunjukkan bahwa model Double Exponential Smoothing (DES) memiliki Mean Absolute Percentage Error (MAPE) sebesar 6,6%, lebih rendah dibandingkan MAPE Bayesian Structural Time Series (BSTS) yang mencapai 7,39%. MAPE yang lebih rendah pada Double Exponential Smoothing (DES) menunjukkan kemampuannya yang lebih baik dalam mengidentifikasi pola data dan menghasilkan perkiraan yang lebih akurat. Meskipun BSTS mampu menangkap komponen musiman dan Trend dengan teknik probabilistik yang canggih, hasil ini menegaskan bahwa Double Exponential Smoothing (DES) adalah metode yang lebih efektif untuk memprediksi angka kemiskinan di Provinsi Gorontalo.
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.
Co-Authors Abubakar, Agung Sucipto Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Alvitha Habibie Armayani Arsal Asriadi Asriadi Asriadi Asriadi Buhungo, Rayhanul Jannah Burhan, Dewinto Demas Novaleda Abdul Karim Dewi Rahmawaty Isa Djihad Wungguli Djuma, Clara Anggriani Doholio, Nadya Pratiwi Emiro, Awalia Hamani, Mohamad Taufik Harmain, Ismail Saputra R. Harun, Trieke Nurfadilah Hasan S. Panigoro HASIRU, LISA SYAHRIA Hinelo, Ikrar Prasetyo I Wayan Can Aryasandi Indrawati Lihawa Ismail Djakaria Ivan Novri Jefriyanto Ibrahim Karim, Finansiya S. Abd. Kasim, Afrianto Pratama Khairun Nisa Humolungo Kilo, Nur Ain Kurniawan Kurniawan La Ode Nashar Lailany Yahya Lapitung, Putri Mohamad, Ramla Mokodompit, Marcela Muhammad, Nur Miftah Nadhilah, Farhah Nanda Arista Rizki Nashar, Laode NISKY IMANSYAH YAHYA Novianita Achmad Nur Falaq Nur Siyam Djibu Nurdin, Sri Ayu Nurul Afifah Nurwan Nurwan Nurwan, Nurwan Pakaya, Desya Neydi Putri Pomahiya, Saiful Rahmi, Emli Resmawan Resmawan Salmun K. Nasib Sampe, Nofia Sigar, Tesya Siti Nurmardia Abdussamad Suci Tilome Suharmin, Wandayana Nur'Amanah Sumarno Ismail Thesalonica Graina Barung Ulopo, Asrul S Usman, Rahmat Setiawan Wewengkang, Hanz Franklyn Bachruddin Winni A. Pakaya wolah, Meitasya