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All Journal EKSAKTA: Journal of Sciences and Data Analysis Statistika Jurnal Sains dan Seni ITS IPTEK Journal of Science IPTEK Journal of Proceedings Series Jurnal Statistika Universitas Muhammadiyah Semarang CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Natural A Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Indonesian Journal of Electrical Engineering and Computer Science Inferensi Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Bosowa Journal of Education Indonesian Journal of Statistics and Its Applications
Irhamah -
Departemen Satistika, Institut Teknologi Sepuluh Nopember, Surabaya 60111
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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ESTIMASI PARAMETER PADA MODEL COX MULTIVARIAT DENGAN METODE MAXIMUM PARTIAL LIKELIHOOD ESTIMATION Irfan Wahyudi; Purhadi Purhadi; Sutikno Sutikno; Irhamah Irhamah
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 4 No 1 (2012): Jurnal Ilmiah Matematika dan Pendidikan Matematika
Publisher : Jurusan Matematika FMIPA Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2012.4.1.2954

Abstract

Multivariate Cox proportional hazard models have ratio property, that is the ratio of hazard functions for two individuals with covariate vectors z1 and z2 are constant (time independent). In this study we talk about estimation of prameters on multivariate Cox model by using Maximum Partial Likelihood Estimation (MPLE) method. To determine the appropriate estimators that maximize the ln-partial likelihood function, after a score vector and a Hessian matrix are found, numerical iteration methods are applied. In this case, we use a Newton Raphson method. This numerical method is used since the solutions of the equation system of the score vector after setting it equal to zero vector are not closed form. Considering the studies about multivariate Cox model are limited, including the parameter estimation methods, but the methods are urgently needed by some fields of study related such as economics, engineering and medical sciences. For this reasons, the goal of this study is designed to develop parameter estimation methods from univariate to multivariate cases.
Analisis Sentimen Masyarakat Indonesia Mengenai Vaksin COVID-19 pada Media Sosial Twitter Menggunakan Metode Naïve Bayes Classifi-er dan Support Vector Machine Rizka Widya Permatasari; Irhamah Irhamah
Jurnal Sains dan Seni ITS Vol 11, No 2 (2022)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373520.v11i2.73995

Abstract

World Health Organization (WHO) mendeklarasi-kan virus COVID-19 sebagai pandemi global pada 11 Maret 2020. Kondisi tersebut memberikan dampak langsung kepada seluruh masyarakat di dunia, dengan mulai diberlakukannya protokol ke-sehatan yang harus diterapkan pada seluruh aspek kegiatan, mulai dari pembatasan sosial hingga lockdown total yang menghambat seluruh kegiatan masyarakat. Salah satu cara yang dilakukan untuk mencegah penyebaran virus ini adalah dengan pemberian vaksin. Kegiatan vaksinasi mulai diberikan kepada masyarakat Indonesia pada bulan Januari 2021. Pada media sosial twitter, pro kontra vaksin COVID-19 sempat menjadi trending topic sehingga dirasa perlu untuk dilakukan penelitian tentang sentimen publik terhadap adanya kegiatan vaksinasi dalam memu-tus rantai penyebaran COVID-19 di Indonesia. Pada penelitian ini digunakan analisis klasifikasi teks yaitu Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). NBC telah banyak digu-nakan dalam pe-nelitian mengenai Text Mining karena memiliki algoritma yang sederhana namun dapat menghasilkan akurasi yang tinggi, se-dangkan SVM memiliki kemampuan yang baik dalam mengolah data berdimensi besar dengan hasil yang efektif. Perbandingan kedua metode menggunakan 10 fold-stratified cross validation dengan kriteria kebaikan klasifikasi AUC dan akurasi menunjuk-kan bahwa SVM memiliki kinerja klasifikasi yang lebih baik di-banding NBC dan SVM kernel menghasilkan ketepatan klasifikasi lebih tinggi dibanding SVM kernel RBF.
Categorical encoder based performance comparison in pre-processing imbalanced multiclass classification Wiyli Yustanti; Nur Iriawan; Irhamah Irhamah
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1705-1715

Abstract

The contribution of this study is to offer suggestions for coding techniques for categorical predictor variables and comprehensive test scenarios to obtain significant performance results for imbalanced multiclass classification problems. We modify scenarios in the data mining process with the sample, explore, modify, model, and assess (SEMMA) framework coupled with statistical hypothesis testing to generalize the model performance evaluation conclusions as enhanced-SEMMA. We selected four open-source data sets with unequal class distributions and categorical predictors. Ordinal, nominal, dirichlet, frequency, target, leave one, one hot, dummy, binary, and hashing encoder methods are used. We use the grid-search technique to find the best hyperparameters. The F1-Score and area under the curve (AUC) are evaluated to select the optimal model. In all datasets with 10-fold stratified cross-validation and 95% to 99% accuracy for each dataset, the results show that support vector machine (SVM) outperforms the decision tree (DT) K-nearest neighbor (KNN), Naïve Bayes (NB), logistic regression (LR), and random forest (RF) algorithms. Probability-based or binary encodings, such as target, Dirichlet, dummy, one-hot, or binary, are best for situations with less than 3% of minor class proportions. Nominal or ordinal encoders are preferred for data with a minor class proportion of more than 3%.
ESTIMASI PARAMETER PADA MODEL COX MULTIVARIAT DENGAN METODE MAXIMUM PARTIAL LIKELIHOOD ESTIMATION Irfan Wahyudi; Purhadi Purhadi; Sutikno Sutikno; Irhamah Irhamah
Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Vol 4 No 1 (2012): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2012.4.1.2954

Abstract

Multivariate Cox proportional hazard models have ratio property, that is the ratio of hazard functions for two individuals with covariate vectors z1 and z2 are constant (time independent). In this study we talk about estimation of prameters on multivariate Cox model by using Maximum Partial Likelihood Estimation (MPLE) method. To determine the appropriate estimators that maximize the ln-partial likelihood function, after a score vector and a Hessian matrix are found, numerical iteration methods are applied. In this case, we use a Newton Raphson method. This numerical method is used since the solutions of the equation system of the score vector after setting it equal to zero vector are not closed form. Considering the studies about multivariate Cox model are limited, including the parameter estimation methods, but the methods are urgently needed by some fields of study related such as economics, engineering and medical sciences. For this reasons, the goal of this study is designed to develop parameter estimation methods from univariate to multivariate cases.
Kolaborasi Guru Dan Orang Tua Terhadap Motivasi Dan Hasil Belajar Membaca Siswa SD Di Kompleks Bayang Kota Makassar Irhamah, Irhamah; Asdar, Asdar; Madjid, Syahriah
Jounal of Primary Education Vol 5 No 1 (2024): Bosowa Journal of Education, Desember 2024
Publisher : Postgraduate Bosowa University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35965/bje.v5i1.5413

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh kolaborasi antara guru dan orang tua terhadap motivasi dan hasil belajar membaca siswa SD di Kompleks Bayang Kota Makassar. Jenis penelitian yang digunakan adalah studi korelasional kuantitatif, dengan sampel penelitian sebanyak 30 siswa yang berasal dari kelas II-A dan II-B UPT SPF SD Negeri Bayang dan UPT SPF SD Inpres Barombong II. Data dikumpulkan melalui kuesioner yang diisi oleh guru dan orang tua siswa, serta tes kemampuan membaca untuk mengukur hasil belajar siswa. Hasil penelitian menunjukkan bahwa terdapat pengaruh positif yang signifikan antara kolaborasi guru dan orang tua terhadap motivasi dan hasil belajar membaca siswa. Berdasarkan output uji hipotesis, nilai signifikansi (2-tailed) sebesar 0,00 yang lebih kecil dari 0,05, sehingga H0 ditolak dan Ha diterima. Ini menunjukkan bahwa kolaborasi yang baik antara guru dan orang tua dapat meningkatkan motivasi belajar siswa dan menghasilkan peningkatan yang signifikan dalam kemampuan membaca mereka. Dengan demikian, penelitian ini menyimpulkan bahwa keterlibatan aktif orang tua dalam proses pembelajaran, yang didukung oleh peran guru yang tepat, berkontribusi positif terhadap pencapaian hasil belajar membaca siswa. Kolaborasi ini penting untuk menciptakan lingkungan belajar yang mendukung dan memotivasi siswa untuk mencapai hasil belajar yang optimal. This study aims to determine the effect of collaboration between teachers and parents on the motivation and reading learning outcomes of elementary school students in the Bayang Complex, Makassar City. The type of research used was a quantitative correlational study, with a research sample of 30 students from classes II-A and II-B UPT SPF SD Negeri Bayang and UPT SPF SD Inpres Barombong II. Data were collected through questionnaires filled out by teachers and parents, as well as reading ability tests to measure student learning outcomes. The results showed that there is a significant positive influence between teacher and parent collaboration on students' motivation and reading learning outcomes. Based on the hypothesis testing output, the significance value (2-tailed) of 0.00 is smaller than 0.05, so H0 is rejected and Ha is accepted. This shows that good collaboration between teachers and parents can increase students' learning motivation and result in a significant improvement in their reading ability. Thus, this study concludes that the active involvement of parents in the learning process, supported by the appropriate role of teachers, contributes positively to the achievement of students' reading learning outcomes. This collaboration is important to create a supportive learning environment and motivate students to achieve optimal learning outcomes.
Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm N Cahyani; Sinta Septi Pangastuti; K Fithriasari; Irhamah Irhamah; N Iriawan
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p396-404

Abstract

A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.
Penerapan Metode Hybrid Autoregressive Integrated Moving Average - Support Vector Regression (ARIMA-SVR) dalam Peramalan Harga Bitcoin Andayuri, Naufal Raihan; Irhamah, Irhamah
Jurnal Sains dan Seni ITS Vol 13, No 5 (2024)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373520.v13i5.152491

Abstract

Pada era saat ini, kemajuan teknologi berkembang pesat, termasuk di sektor ekonomi dan keuangan. Salah satu inovasi penting adalah cryptocurrency seperti Bitcoin, yang di-dukung oleh teknologi blockchain yang meningkatkan trans-paransi dalam pelacakan pembayaran digital. Meskipun memi-liki risiko tinggi, Bitcoin juga menawarkan potensi keuntungan besar jika dikelola dengan baik. Dengan jumlah Bitcoin yang terbatas dan nilai tukarnya yang cenderung meningkat, analisis fluktuasi harga Bitcoin menjadi penting untuk mengurangi risiko investasi. Penelitian ini bertujuan untuk memodelkan dan meramalkan harga Bitcoin menggunakan model ARIMA untuk menangkap pola linear, SVR untuk menangkap pola non linear, dan model Hybrid ARIMA-SVR. Keakuratan model dievaluasi menggunakan RMSE dan MAPE. Hasil menunjukkan model ARIMA(1,1,1) menghasilkan RMSE 850,92 dan MAPE 1,559%, sementara SVR dengan kernel RBF menghasilkan RMSE 841,14 dan MAPE 1,516%. Model hybrid ARIMA-SVR dengan ARIMA(1,1,0) menghasilkan RMSE 832,90 dan MAPE 1,510%, menjadikannya yang terbaik untuk meramalkan harga Bitcoin selama 7 hari ke depan. Namun, masalah time lag yang terjadi perlu diperhatikan meskipun akurasi model tinggi, karena me-nyebabkan prediksi sering kali terlambat sehingga dapat me-ngurangi validitas peramalan.
Sistem Rekomendasi Buku Bacaan untuk Anak Menggunakan Collaborative Filtering dan Topic Modelling Priady, Farrel Edgarrafi; Irhamah, Irhamah; Widhianingsih, Tintrim Dwi Ary
Jurnal Sains dan Seni ITS Vol 13, No 6 (2024)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373520.v13i6.155082

Abstract

Membaca buku merupakan salah satu kegiatan pen-ting dalam perkembangan anak, terutama pada usia emas (0–6 tahun). Namun, di era digital, anak-anak dihadapkan dengan berbagai rangsangan yang kuat, sehingga penting untuk mem-berikan bacaan yang sesuai dengan minat dan usia mereka. Pe-ngembangan sistem rekomendasi buku bacaan untuk anak menjadi suatu kebutuhan mendesak guna meningkatkan minat dan kualitas literatur anak di Indonesia. Penelitian ini ber-tujuan untuk membangun sistem rekomendasi buku bacaan untuk anak menggunakan dua pendekatan, yaitu collaborative filtering dan topic modelling. Data yang digunakan adalah data judul, deskripsi, dan rating buku yang diambil dari website Goodreads yang disediakan oleh University of California San Diego (UCSD). Berdasarkan hasil penelitian yang telah dilakukan menggunakan Grid Search Cross Validation dengan 5 fold, didapatkan bahwa model terbaik adalah sistem rekomendasi meng-gunakan faktorisasi matriks SVD dengan nilai evaluasi dari model tersebut adalah RMSE sebesar 0,7941, accuracy sebesar 79,89%, dan F1-Score sebesar 88,28%. Model tersebut lebih baik daripada metode pembanding yaitu LDA First dengan nilai evaluasi dari model tersebut adalah RMSE sebesar 0,9011, accuracy sebesar 78,31%, dan F1-Score sebesar 87,81%. Penelitian selanjutnya disarankan untuk melakukan hibridisasi atau penggabungan dari metode SVD dan LDA tersebut secara bersamaan, juga menambah data pengguna seperti umur, jenis kelamin, atau lokasi dari pengguna untuk menangani cold start.
Hybrid Geometric Brownian Motion-Markov Switching untuk Peramalan Harga Saham Indonesia Hamdani, Aldan Maulana; Iriawan, Nur; Irhamah, Irhamah
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.35300

Abstract

This study aims to analyse the performance of a stock price forecasting model based on Geometric Brownian Motion (GBM) modified with a Markov Switching (MS) approach. The research gap addressed is the limitation of the classical GBM model, which assumes constant volatility and is therefore unable to capture sudden changes in market regimes. To address these limitations, this study proposes a hybrid GBM-MS model as its main scientific contribution, in whichthe drift and volatility parameters are dynamically estimated following changes in market conditions through a switching mechanism between regimes. Parameter estimation is performed using the Hidden Markov Model. The model's performance is compared with the classical GBM as a benchmark. The research uses daily closing price data of PT Bank Central Asia Tbk. (BBCA) shares for the period 1 July – 30 December 2024. The results show that the hybrid GBM-MS model provides better forcasting accuracy with a MAPE value of 2.25% on training data and 1.38% on testing data, lower than the classical GBM model. These findings confirm that the integration of Markov Switching enhances the model's adaptability in capturing structural changes and market volatility. Practically, the hybrid GBM-MS model can be used as a more reliable forecasting and risk management tool to support investment decision-making, especially in dynamic and unstable market environments.
Co-Authors Adatul Mukarromah Adi Slamet Kusumawardana Almuhayar, Mawanda Andayuri, Naufal Raihan Ani Budi Astuti Anindya Apriliyanti Pravitasari Arif Nur Wijiyanto Asdar Asdar Ayundyah Kesumawati Bagas Wahyu Yoga Priambodo Cicilia Ajeng Pratiwi Devi Putri Isnarwaty Dona Adityas Dwi E. Kusrini Dwi Endah Kusrini Dwi Listya Nurina Eka Apriliani Eliya Ainul Farri Elly Pusporani Erna Dwi Nurindah Sari Faiqah, Elok Ferriastuti, Widiana Fithriasari, Kartika Gede Suwardika Hamdani, Aldan Maulana Hanna Silia Kartidan Hendro Nurhadi Heri Kuswanto Heri Kuswanto Ilafi Andalita Irfan Wahyudi Isnaini P Dewi Jefry Pranata Maulana K Fithriasari Kiki Wulan Suci Kinanti Hanugera Gusti Mashuri Mashuri Mochammad Farros Fatchur Roji Moh Ali Asfihani Mohammad Naufal Abdullah Muhammad Abid As Sarofi muhammad mashuri N Cahyani N Iriawan Nadhifa Ayu Shafirra Ni Luh Putu Ika Candrawengi Ni Putu Budi Setianingsih Nur Aini Rakhmawati Nur Iriawan Nur Iriawan Nursya’bani Hendro Prabowo Paramita, NLP Satyaning Pradnya Pratnya Paramitha Oktaviana Priady, Farrel Edgarrafi Purhadi Purhadi Ramadhana Dio Gradianta Reza Mustofa Rizka Widya Permatasari Rr. Sekar Kalaksita Santi Wulan Purnami Sinta Septi Pangastuti Siti Qomariyah Sulistianingrum Sulistianingrum Sutikno Sutikno Syahriah Madjid Syarifah Diana Permai Taufik Azmi Tesalonika Putri Wahyu Wulan Fitriah Widhianingsih, Tintrim Dwi Ary Wirajaya Kusuma Wiwiek Setya Winahju Wiyli Yustanti Yulita Nurvitasari