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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

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Implementasi Metode Bidirectional LSTM Dengan Word Embedding FastText Dalam Analisis Sentimen Ulasan Pengguna Aplikasi Maxim Wewengkang, Hanz Franklyn Bachruddin; Wungguli, Djihad; Yahya, Nisky Imansyah; Hasan, Isran K.; Abdussamad, Siti Nurmardia
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.v4i5.33358

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 Harun, Trieke Nurfadilah; Djakaria, Ismail; Yahya, Nisky Imansyah; Nasib, Salmun K; Hasan, Isran K
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.v4i5.33303

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.
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.
Prediksi Wisatawan Mancanegara di Indonesia Menggunakan Metode SARIMAX dengan Efek Variasi Kalender Libur Nasional Pakaya, Desya Neydi Putri; Achmad, Novianita; Hasan, Isran K; Wungguli, Djihad; Abdussamad, Siti Nurmardia
Jurnal Riset Mahasiswa Matematika Vol 4, No 6 (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.v4i6.34937

Abstract

Fluctuations in the number of foreign tourist arrivals often produce outlier values that can interfere with the accuracy of the forecasting model. This study uses a boxplot approach to detect outliers, followed by Natural Logarithm (ln) transformation as a treatment step. The Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) method is applied by considering three exogenous variables that show the effect of variations in the National Holiday calendar in the form of Nyepi Day, Idul Fitri Day and year-end holidays. The results of the analysis show that the three variables have a positive effect on the increase in the number of foreign tourist arrivals, where Nyepi Day makes the largest contribution compared to the other two holiday periods. Model 2 (0,1,1)(1,0,1)[12] was selected as the most optimal model based on the evaluation results of several models that have been compared. This model shows excellent performance, indicated by the Mean Absolute Percentage Error (MAPE) value of 3.75\% which indicates that the model has very high prediction accuracy. So that the SARIMAX model is effective in modeling and predicting the number of foreign tourist visits in Indonesia.
RAINBOW CONNECTION NUMBER AND TOTAL RAINBOW CONNECTION NUMBER OF AMALGAMATION RESULTS DIAMOND GRAPH(〖Br〗_4) AND FAN GRAPH(F_3) Ismail, Sumarno; Hasan, Isran K.; Sigar, Tesya; Nasib, Salmun K.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (980.35 KB) | DOI: 10.30598/barekengvol16iss1pp023-030

Abstract

If be a graph and edge coloring of G is a function , rainbow connection number is the minimum-k coloration of the rainbow on the edge of graph G and denoted by rc(G). Rainbow connection numbers can be applied to the result of operations on some special graphs, such as diamond graphs and fan graphs. Graph operation is a method used to obtain a new graph by combining two graphs. This study performed amalgamation operations to obtain rainbow connection numbers and rainbow-total-connection numbers in diamond graphs ( ) and fan graphs ( ) or . Based on the research, it is obtained that the rainbow-connection number theorem on the amalgamation result of the diamond graph ( ) and fan graph ( is with . Furthermore, the theorem related to the total rainbow-connection number on the amalgamation result of the diamond graph( ) and the fan graph ( is obtained, namely with .
Prediksi Harga Emas Dunia Menggunakan Deep Learning GRU dengan Optimasi Nadam Harmain, Ismail Saputra R.; Nurwan, Nurwan; Hasan, Isran K.; Wungguli, Djihad; Yahya, Nisky Imansyah
Jurnal Riset Mahasiswa Matematika Vol 4, No 6 (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.v4i6.36007

Abstract

Volatilitas harga emas yang tinggi menuntut adanya metode prediksi yang andal untuk mendukung pengambilan keputusan investasi. Penelitian ini mengimplementasikan algoritma Gated Recurrent Unit (GRU) berbasis deep learning yang dioptimalkan menggunakan Nesterov-Accelerated Adaptive Moment Estimation (Nadam) untuk memprediksi harga emas harian.Model terbaik diperoleh dengan nilai Mean Squared Error (MSE) sebesar 0, 00012 pada data univariat dan 0, 00027 pada data multivariat. Mean Absolute Percentage Error (MAPE) yang diperoleh masing-masing sebesar 1,107% untuk data univariat dan 1,59% untuk data multivariat. Hasil tersebut mengindikasikan bahwa model GRU dengan optimasi Nadam memiliki performa prediksi yang tinggi, baik pada data deret waktu tanpa penambahan fitur maupun dengan penambahan fitur.
Co-Authors Abubakar, Agung Sucipto Agusyarif Rezka Nuha Alaina, Silvana Rahmayanti Alvitha Habibie Armayani Arsal Asriadi Asriadi Asriadi Asriadi Buhungo, Rayhanul Jannah 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 Mokodompit, Marcela Nadhilah, Farhah Nanda Arista Rizki NISKY IMANSYAH YAHYA Novianita Achmad Nur Falaq 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 Suharmin, Wandayana Nur'Amanah Sumarno Ismail Thesalonica Graina Barung Ulopo, Asrul S Usman, Rahmat Setiawan Wewengkang, Hanz Franklyn Bachruddin Winni A. Pakaya wolah, Meitasya