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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 Metode K-Medoids Clustering untuk Mengelompokkan Kecenderungan Menonton Drama Korea Suryahaty Aisyah Aulia Kaluku; Muhammad Rezky Friesta Payu; Isran K. Hasan
Griya Journal of Mathematics Education and Application Vol. 6 No. 2 (2026): Juni 2026
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v6i2.1066

Abstract

The phenomenon of watching Korean dramas is increasingly growing among female university students and has the potential to create differences in viewing behavior patterns with varying levels of intensity. This study aims to determine the optimal number of clusters using the Elbow method and to identify respondent grouping patterns based on similarities in Korean drama viewing behavior using the K-Medoids method. In addition, this study evaluates the quality of the formed clusters using an internal validation method, namely the Silhouette Coefficient. The data used are primary data obtained through the distribution of questionnaires to female students at Universitas Negeri Gorontalo, incorporating aspects that represent the intensity and tendencies of Korean drama viewing behavior. The analysis begins with determining the number of clusters using the Elbow method based on changes in the Sum of Squared Errors (SSE). The results show that 4 clusters represent the most representative number of clusters visually. Furthermore, the K-Medoids method is applied to group respondents into 4 clusters based on similarities in their Korean drama viewing behavior. However, the evaluation results using the Silhouette Coefficient indicate that the quality of the formed clusters tends to be low. This is reflected in the variation of silhouette coefficient values, where only one cluster demonstrates good clustering quality, while the others exhibit weaker structures with unclear separation between clusters. This condition indicates the presence of data overlap among clusters, resulting in less distinct cluster boundaries
Penerapan Metode Fuzzy Time Series Chen Orde Tinggi Pada Peramalan Nilai Tukar Petani Provinsi Gorontalo Nur Miftah Muhammad; Isran K. Hasan; Armayani Arsal; Emli Rahmi; Laode Nashar
Jurnal Riset Mahasiswa Matematika Vol 5, No 4 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i4.41337

Abstract

Nilai Tukar Petani (NTP) merupakan salah satu indikator ekonomi yang digunakan untuk menggambarkan tingkat kesejahteraan petani dan kondisi sektor pertanian. Pergerakan nilai NTP yang bersifat fluktuatif memerlukan pendekatan peramalan yang mampu menangkap pola data secara memadai. Penelitian ini bertujuan menerapkan metode Fuzzy Time Series (FTS) Chen orde tinggi untuk meramalkan Nilai Tukar Petani di Provinsi Gorontalo serta mengidentifikasi model orde yang memberikan tingkat kesalahan peramalan yang paling tepat. Data yang digunakan berupa data bulanan NTP Provinsi Gorontalo periode Januari 2020 hingga Oktober 2025 yang terdiri dari 70 observasi dan diperoleh dari publikasi resmi Badan Pusat Statistik. Data dibagi menjadi 80% data latih dan 20% data uji menggunakan pendekatan pembagian berdasarkan waktu. Tahapan analisis meliputi penentuan himpunan semesta, pembentukan interval, proses fuzzifikasi, pembentukan Fuzzy Logical Relationship (FLR) dan Fuzzy Logical Relationship Group (FLRG), defuzzifikasi, serta evaluasi kinerja model menggunakan Mean Absolute Percentage Error (MAPE). Hasil analisis menunjukkan bahwa model FTS Chen orde dua menghasilkan nilai MAPE sebesar 3,3164% pada data uji, yang lebih kecil dibandingkan dengan model orde satu. Sementara itu, model orde tiga tidak dapat digunakan secara optimal karena tidak terbentuk hubungan fuzzy pada beberapa periode data pengujian. Hasil ini menunjukkan bahwa pendekatan FTS Chen orde dua dapat memberikan hasil peramalan yang relatif lebih baik pada data NTP yang dianalisis dalam penelitian ini.
Perbandingan Metode ARFIMA dan Metode ARIMA-FFNN (Studi Kasus: Harga Saham di PT. Telekomunikasi Indonesia Tbk) Afandi W. Biga; Isran K. Hasan; Nurwan
Research Review: Jurnal Ilmiah Multidisiplin Vol. 4 No. 2 (2025): Research Review: Jurnal Ilmiah Multidisiplin (Agustus 2025 - Januari 2026)
Publisher : Transbahasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54923/researchreview.v4i2.221

Abstract

This study aims to compare the effectiveness of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model and the Autoregressive Integrated Moving Average–Feedforward Neural Network (ARIMA-FFNN) hybrid model in forecasting the stock price of PT Telekomunikasi Indonesia Tbk. Forecasting stock prices is a crucial aspect of financial decision-making since accurate predictions can support investors and policymakers in minimizing risks and maximizing returns. In this study, the ARFIMA(1,d,1) model and the ARIMA(0,d,2)-FFNN(0,2) hybrid model were applied to historical daily stock price data of PT Telekomunikasi Indonesia Tbk. The performance of both models was evaluated using the Mean Absolute Percentage Error (MAPE), which is widely recognized as a reliable metric for measuring prediction accuracy. The results revealed that the ARFIMA(1,d,1) model generated a MAPE value of 2.11%, while the ARIMA(0,d,2)-FFNN(0,2) model achieved a significantly lower MAPE value of 1.28%. These findings indicate that the hybrid ARIMA-FFNN approach provides more accurate forecasting results compared to the ARFIMA model. Therefore, the ARIMA(0,d,2)-FFNN(0,2) model can be considered a more optimal and reliable forecasting method for predicting stock prices in PT Telekomunikasi Indonesia Tbk. The results of this study highlight the potential of combining traditional time series models with machine learning approaches to enhance forecasting accuracy in financial markets.
Perbandingan Gaussian Process Regression dan Support Vector Regression dalam Prediksi Suhu Perencanaan Tanam Jagung Misranti A. Samulu; Novianita Achmad; Isran K. Hasan
Jambura Journal of Probability and Statistics Vol 7, No 1 (2026): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v7i1.38097

Abstract

Corn is an important food crop commodity that plays a significant role in the agricultural sector and regional economy. Efforts to increase corn production in Gorontalo have become one of the programs initiated by the Ministry of Agriculture to support Indonesia's corn exports. However, corn productivity is influenced by various factors, one of which is temperature variation resulting from climate change. This study aims to predict weekly maximum temperatures as a basis for determining the optimal planting time for corn by comparing the performance of the \textit{Gaussian Process Regression} (GPR) method using four kernel functions (\textit{Periodic}, \textit{Matern}, \textit{Radial Basis Function}, and \textit{Rational Quadratic}) and the \textit{Support Vector Regression} (SVR) method optimized using \textit{Particle Swarm Optimization} (PSO). The data used in this study consist of weekly maximum temperature observations from 2023 to 2024 obtained from the Gorontalo Climatology Station. The results indicate that the GPR method achieved the best performance, yielding a \textit{Mean Absolute Percentage Error} (MAPE) of 2.17\%, while the PSO-optimized SVR method produced a MAPE of 2.30\%. Based on the forecasting results, the optimal corn planting period within the next 30 weeks is between the sixth and eighth weeks, as the critical growth phase of the crop is expected to occur under the most stable temperature conditions and within the optimal temperature range for corn growth. 
Penerapan Model Word Embedding IndoBERTweet pada Metode Support Vector Machine untuk Klasifikasi Opini Publik di Media Sosial X Naufal Daffa Pahrun; Novianita Achmad; Isran K Hasan
Jambura Journal of Probability and Statistics Vol 7, No 1 (2026): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v7i1.38992

Abstract

This study aims to classify public sentiment regarding the redenomination of the Indonesian rupiah on social media platform X using a combination of Support Vector Machine (SVM) and IndoBERTweet word embedding. The main challenge in social media sentiment analysis lies in unstructured text, informal language usage, and contextual ambiguity. Therefore, an approach capable of capturing contextual meaning while maintaining high classification accuracy is required. This research employs a quantitative approach, including data collection through crawling, text preprocessing, data labeling, feature extraction using IndoBERTweet, and classification using SVM with a Radial Basis Function (RBF) kernel. A total of 1,014 tweets were collected and refined into 370 labeled data consisting of positive and negative sentiments. The results show that the proposed model achieves an accuracy of 82\%, precision of 83\%, recall of 82\%, and F1-score of 82\%. These findings indicate that the integration of IndoBERTweet and SVM effectively captures contextual semantics in Indonesian social media text and improves sentiment classification performance. Furthermore, the analysis reveals that the majority of public opinions tend to be positive toward the redenomination issue. This study is expected to contribute to the development of machine learning-based sentiment analysis and support more responsive policy-making based on public opinion   
Pendekatan Hybrid VARIMA–ANN untuk Peramalan Multivariat Data Cuaca Bulanan di Provinsi Gorontalo Nur Anggraini T. Ali; Djihad Wungguli; Isran K. Hasan
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.37513

Abstract

Multivariate time series forecasting is essential for understanding the interrelationships among weather parameters. This study aims to develop a multivariate forecasting model using a hybrid Vector Autoregressive Integrated Moving Average (VARIMA)–Artificial Neural Network (ANN) approach with the backpropagation algorithm, applied to weather data from Gorontalo Province over the 2015–2023 period, including air temperature, humidity, and wind speed. The data were divided into training data (2015–2021) and testing data (2022–2023). The VARIMA model was employed to capture the linear component, while the residuals from the VARIMA model were subsequently modeled using ANN to capture nonlinear patterns. The order of the VARIMA model was determined based on the smallest Akaike Information Criterion (AIC) value, while model performance was evaluated using Mean Absolute Percentage Error (MAPE). The results indicate that the best-performing model is VARIMA(5,1,1)–ANN(18,9,3), with MAPE values of 1.32% for air temperature, 20.54% for humidity, and 21.96% for wind speed. These findings suggest that the hybrid VARIMA–ANN approach provides good forecasting performance and has the potential to serve as an alternative method for multivariate weather forecasting.   
Wind Speed Category Characteristics in Bone Bolango Regency: A Markov Chain Approach Using the Beaufort Scale and Metropolis-Hastings Algorithm Saiful Pomahiya; Nurwan Nurwan; Nisky Imansyah Yahya; Salmun K. Nasib; Isran K. Hasan; Asriadi Asriadi
Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 2 (2024): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol3iss2pp63-68

Abstract

This study models daily wind speed transitions in the Bone Bolango Regency using the Markov Chain Monte Carlo (MCMC) method and the Metropolis-Hastings algorithm, employing the Beaufort scale for wind speed classification. The research aims to predict the steady-state distribution of wind speeds and evaluate their temporal stability. Daily wind speed data from 2023, provided by the Meteorology, Climatology, and Geophysics Agency (BMKG), were categorized into three levels: calm, light breeze, and fresh breeze, based on the Beaufort scale. Transition probabilities were estimated using the Beta distribution, and simulations via the Metropolis-Hastings algorithm yielded the steady-state distribution. Results show a significant tendency for transitions from calm and light breeze categories to fresh breezes, with varying probabilities. Notably, calm conditions exhibit a 69% likelihood of transitioning to a light breeze. This research contributes to improving wind speed prediction models by integrating statistical algorithms with meteorological classifications. The findings have implications for enhancing short-term weather forecasts and developing predictive systems for regions with similar weather patterns.
Implementasi Metode Bidirectional LSTM Dengan Word Embedding FastText Dalam Analisis Sentimen Ulasan Pengguna Aplikasi Maxim Hanz Franklyn Bachruddin Wewengkang; Djihad Wungguli; Nisky Imansyah Yahya; Isran K. Hasan; Siti Nurmardia Abdussamad
Jurnal Riset Mahasiswa Matematika Vol 4, No 5 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i5.33358

Abstract

Aplikasi transportasi online kini menjadi bagian penting dalam kehidupan masyarakat Indonesia. Maxim, sebagai salah satu penyedia layanan, perlu memahami persepsi pengguna untuk meningkatkan kualitas layanannya. Penelitian ini menerapkan metode Bidirectional Long Short-Term Memory (BiLSTM) untuk melakukan klasifikasi sentimen terhadap ulasan pengguna aplikasi Maxim di Google Play Store. Untuk memperkuat representasi kata, digunakan word embedding FastText yang mampu menangkap informasi sub-kata secara lebih baik. Data penelitian diperoleh melalui scraping menggunakan package google-play-scraper pada Python. Model BiLSTM yang dilatih dengan konfigurasi hyperparameter optimal berhasil mengklasifikasikan sentimen ulasan secara efektif, dengan hasil accuracy 94%, precision 96%, recall 95%, dan f1-score 95%. Hasil ini menunjukkan bahwa kombinasi BiLSTM dan FastText mampu mendeteksi sentimen positif dan negatif secara akurat dan seimbang, serta relevan untuk mendukung evaluasi kualitas layanan berbasis opini pengguna.
Penerapan Model ARFIMA-LSTM Menggunakan Variasi Estimasi Parameter Pembeda Dalam Meramalkan data IHPBI Trieke Nurfadilah Harun; Ismail Djakaria; Nisky Imansyah Yahya; Salmun K Nasib; Isran K Hasan
Jurnal Riset Mahasiswa Matematika Vol 4, No 5 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i5.33303

Abstract

Indeks Harga Perdagangan Besar Indonesia (IHPBI) merupakan indikator penting dalam mengukur perkembangan ekonomi, khususnya pada sektor pertanian yang memiliki pengaruh besar terhadap daya beli masyarakat. Fluktuasi harga di sektor ini berdampak langsung pada kesejahteraan konsumen dan produsen, sehingga diperlukan metode peramalan yang akurat. Penelitian ini bertujuan untuk meramalkan IHPBI sektor pertanian menggunakan pendekatan hybrid Autoregressive Fractionally Integrated Moving Average (ARFIMA) dan Long Short-Term Memory (LSTM), serta membandingkan performa  metode estimasi parameter pembeda terbaik. Model ARFIMA digunakan untuk menangani komponen stasioner dan pola jangka panjang melalui diferensiasi pecahan, sedangkan LSTM digunakan untuk menangkap pola nonlinier dalam data. Keterbaruan dalam penelitian ini adalah membandingkan parameter pembeda terbaik yaitu Local Whittle dan Rescaled Range Statistics dalam hybrid ARFIMA-LSTM. Hasil dari penelitian yaitu peramalan menunjukkan tren naik IHPBI sektor pertanian selama 12 bulan ke depan. Metode estimasi parameter pembeda terbaik dalam model ARFIMA adalah Rescaled Range Statistics dengan nilai sebesar 0,322. Model hybrid ini menghasilkan nilai MAPE sebesar 0,6337853%, yang menunjukkan tingkat akurasi sangat tinggi.
Implementasi Particle Swarm Optimization pada Fuzzy Time Series Lee untuk Prediksi Nilai Tukar Petani NTT Ramla Mohamad; Novianita Achmad; Isran K. Hasan
Jurnal Riset Mahasiswa Matematika Vol 5, No 2 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i2.37544

Abstract

Nilai Tukar Petani (NTP) merupakan indikator penting untuk mengukur kesejahteraan petani di Indonesia. Data NTP, khususnya di Provinsi Nusa Tenggara Timur (NTT), menunjukkan fluktuasi yang cukup tinggi sehingga diperlukan metode peramalan yang mampu menangani pola data nonlinier dan ketidakpastian. Penelitian ini menerapkan metode Fuzzy Time Series (FTS) Lee yang dioptimasi menggunakan Particle Swarm Optimization (PSO) untuk meningkatkan akurasi peramalan NTP. PSO digunakan untuk menentukan interval fuzzy optimal sehingga proses fuzzifikasi pada FTS Lee menjadi lebih representatif terhadap variasi data. Data yang digunakan merupakan data bulanan NTP Provinsi NTT periode Januari 2021–Desember 2024 yang diperoleh dari Badan Pusat Statistik. Hasil penelitian menunjukkan bahwa model PSO-FTS Lee memberikan tingkat akurasi yang lebih baik dibandingkan FTS Lee tanpa optimasi, dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 0,44\%, lebih rendah dibandingkan 0,46\% pada model standar. Temuan ini membuktikan bahwa integrasi PSO dengan FTS Lee efektif dalam meningkatkan kinerja model peramalan untuk data deret waktu yang bersifat fluktuatif dan nonlinier.
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