Article Per Year (5 Year)
p-Index From 2021 - 2026
2.757
P-Index
This Author published in this journals
All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Telematika : Jurnal Informatika dan Teknologi Informasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jiko (Jurnal Informatika dan komputer) Jurnal Teknologi Terpadu bit-Tech Nusantara Science and Technology Proceedings International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Sewagati Teknologi : Jurnal Ilmiah Sistem Informasi
Muhaimin, Amri
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 17 Documents
Search

 1   2 
RAINFALL FORECASTING WITH AN INTERMITTENT APPROACH USING HYBRID EXPONENTIAL SMOOTHING NEURAL NETWORK Permata, Regita Putri; Muhaimin, Amri; Hidayati, Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0457-0466

Abstract

Rainfall forecasting is crucial in agriculture, water resource management, urban planning, and disaster preparation. Traditional approaches fail to capture complicated and intermittent rainfall patterns. The “Hybrid Exponential Smoothing Neural Network” is introduced in this study to handle intermittent rainfall forecasting issues. Exponential Smoothing, an established approach for discovering underlying patterns and seasonal fluctuations in time series data, is combined with Neural Networks, which are good at capturing complex linkages and nonlinearities. Using these two methods, this model hopes to deliver a complete rainfall forecasting solution that accounts for short-term changes and long-term patterns. This research uses residuals from the exponential smoothing model and is modeled using a Neural Network. The residual input is transformed using rolling mean. The results show that the hybrid model is able to capture patterns well, but there are still patterns that experience time lag. Experimental results obtained reveal that the hybrid methodology performs better than the model exponential smoothing, implying that the proposed model hybrid synergy approach can be used as an alternative solution to the rainfall time series forecasting. The results show that the Hybrid method can form patterns better than individual exponential smoothing models or neural networks. The RMSSE values for all areas are 1.0185, 1.55092, 1.0872.
Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10797

Abstract

Purpose: Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6%, and for the future, the government has set a target of up to 14% in 2024. Rapid technological developments and freedom of expression on the internet produce review text data that can be analyzed for evaluation. This study analyzes the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation.Design/methodology/approach: The methodology used in this study is Latent Dirichlet Allocation. The data was collected from twitter with the keyword 'stunting'. After, the data was cleaned and then modeled using the Latent Dirichlet Allocation.Findings/results: The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'children', 'decrease', 'number', 'prevention', and 'nutrition' are among the words that often appear on stunting.Originality/value/state of the art: This study uses the keyword stunting and analyzes it. Social media analytics show that the people of Indonesia are primarily aware of stunting. Also, the Latent Dirichlet Analysis can be used to create the model.
PERAMALAN MENGGUNAKAN HYBRID SEASONAL ARIMA DAN EXTREME LEARNING MACHINE: STUDI KASUS JUMLAH PRODUKSI BERAS DI PROVINSI JAWA TIMUR Pakpahan, Vera Febrianti; Muhaimin, Amri; Syaifullah, Wahyu
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6673

Abstract

Penelitian ini mengevaluasi performa metode hybrid Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Extreme Learning Machine (ELM) untuk peramalan data deret waktu. Metode SARIMA digunakan untuk menangkap pola musiman dan linier, sedangkan ELM diaplikasikan pada residual prediksi SARIMA untuk mendeteksi pola non-linier yang sulit ditangkap oleh model tradisional. Studi kasus difokuskan pada prediksi produksi beras bulanan di Provinsi Jawa Timur, salah satu lumbung beras nasional dengan fluktuasi produksi yang memengaruhi perencanaan distribusi dan kebijakan pangan. Hasil evaluasi menunjukkan bahwa model hybrid SARIMA–ELM mencapai nilai MAPE sebesar 9,01% dan RMSE sebesar 38.639,93, menunjukkan akurasi prediksi yang baik. Temuan ini menegaskan bahwa kombinasi SARIMA dan ELM dapat menjadi pendekatan yang efektif untuk peramalan deret waktu dengan pola linier dan non-linier, serta memiliki potensi untuk diterapkan pada dataset atau sektor lain yang memiliki karakteristik serupa.
Evaluasi Pembelajaran yang Interaktif dan Inovatif melalui Pemanfaatan Canva dan Permainan Edukasi Digital untuk Guru Bahasa Indonesia Tingkat SMP di Kota Surabaya Hamid, Abdul; Sa'diyah, Ilmatus; Muhaimin, Amri; Rusdianti Maulani Putri, Adinda; Putri Pascha, Dea
Sewagati Vol 9 No 5 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i5.7881

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kapasitasguru Bahasa Indonesia tingkat SMP di Kota Surabaya dalam merancang danmenerapkan asesmen pembelajaran yang interaktif dan inovatif dengan memanfaatkanplatform digital seperti Quizziz, Educandy, Wordwall, Canva, Bamboozle,dan Socrative. Kegiatan ini dilatarbelakangi oleh rendahnya variasi metode asesmenyang digunakan guru, yang umumnya masih bersifat konvensional dan kurang melibatkanaspek afektif serta psikomotor siswa. Program pelatihan dilaksanakan melaluilima tahapan sosialisasi, pelatihan, implementasi, pendampingan, dan keberlanjutanprogram. Metode yang digunakan meliputi pelatihan praktik langsung (learningby doing), pendampingan teknis, serta evaluasi menggunakan pretest, posttest, dankuesioner umpan balik. Hasil pelatihan menunjukkan bahwa lebih dari 95% gurumenyatakan materi pelatihan sangat relevan dengan kebutuhan mereka dan memberikanwawasan baru dalam penyusunan asesmen interaktif. Selain itu, mayoritasguru merasa termotivasi untuk menjadi lebih kreatif dan inovatif dalam merancangasesmen. Hasil ini menunjukkan bahwa kegiatan ini memberikan kontribusi nyatadalam meningkatkan profesionalisme guru dan mendukung implementasi KurikulumMerdeka secara efektif.
Prediksi Viralitas Tweet Berbahasa Indonesia Menggunakan IndoBERTweet, RoBERTa, dan Multi-Layer Perceptron untuk Optimalisasi Strategi Pemasaran Digital Putri, Deannisa Syafira; Muhaimin, Amri; Idhom, Mohammad
Jurnal Ilmiah IT CIDA Vol 11 No 2: Desember 2025
Publisher : STMIK AMIKOM Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55635/jic.v11i2.297

Abstract

Penelitian ini bertujuan memprediksi tingkat viralitas tweet berbahasa Indonesia dengan menggabungkan fitur teks, sentimen, dan numerik melalui model IndoBERTweet, RoBERTa, dan Multi-Layer Perceptron (MLP). IndoBERTweet digunakan untuk menghasilkan representasi semantik, RoBERTa untuk menganalisis polaritas sentimen, dan MLP sebagai klasifikator yang menggabungkan seluruh fitur. Dataset terdiri dari 1.716 tweet promosi pada platform X (27 November 2024–27 Mei 2025), yang setelah pra-pemrosesan dan pelabelan menggunakan Gaussian Mixture Model (GMM) menghasilkan 1.481 data bersih siap latih. Model mencapai performa tinggi dengan akurasi 96,99%, precision 96,97%, recall 96,99%, dan F1-score 96,97%, mencatat peningkatan sebesar 0,32% dibandingkan Linear SVM dan 1,66% dibandingkan Decision Tree. Temuan ini menunjukkan bahwa integrasi representasi semantik dan sentimen secara efektif meningkatkan akurasi prediksi dibandingkan pendekatan tunggal, serta berpotensi membantu praktisi pemasaran digital merancang strategi kampanye yang lebih tepat sasaran dan berpeluang viral.
Sistem Rekomendasi Menu Kantin Menggunakan Lifespan-Aware Association Rule Mining Dengan Hybrid Apriori Dan FP-Growth Navsih, Muhammad Ghinan; Muhaimin, Amri; Wara, Shindi Shella May
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 16 No 1 (2026): January - on progress
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v16i1.6143

Abstract

This study addresses the problem of how to systematically increase cross-selling in a small canteen, where additional items such as drinks and snacks are usually offered only based on the cashier’s memory and intuition. The proposed solution is a point-of-sale (POS) recommendation system that suggests complementary menu items in real time, based on patterns learned from historical transaction data. The system uses a lifespan-aware association rule mining approach with a hybrid of Apriori and FP-Growth, where both algorithms are applied to one-hot encoded POS data and their outputs are combined and validated before being deployed as recommendation rules. The research objectives are to extract stable co-purchase patterns from canteen transactions, compare the computational performance of Apriori and FP-Growth in this real-world setting, and evaluate the practical effectiveness of the resulting recommendation system. The method benchmarks Apriori and FP-Growth across several minimum support values in terms of frequent itemsets count, computation time, and peak memory usage, and then integrates the validated rules into a POS application for real-time inference. The system’s effectiveness is measured using a session-level recommendation acceptance rate, defined as the proportion of transactions that display the recommendation modal and result in at least one recommended item being accepted and paid. The results show that Apriori and FP-Growth consistently produce identical sets of frequent itemsets, but with markedly different computational characteristics: Apriori is significantly faster, while FP-Growth exhibits more stable memory usage. In the deployed setting, the recommendation system achieves a session-level acceptance rate of 15.52% in 3,588 transactions, indicating that roughly one in seven sessions with recommendations leads to an additional item being purchased. Compared to many existing works that focus only on algorithmic performance on benchmark datasets, this research contributes a lifespan-aware, empirically benchmarked hybrid ARM approach that is fully integrated into a working POS system and evaluated using real-world acceptance behavior.
Analysis of the LQ45 Stock Portfolio Using Mean–Variance Method and Cornish–Fisher Expansion Putri, Shafira Amanda; Trimono, Trimono; Muhaimin, Amri
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3640

Abstract

Public interest in stock market investment in Indonesia has increased alongside growing awareness of financial planning and portfolio management. The LQ45 Index, consisting of stocks with high liquidity, large market capitalization, and strong fundamentals, is widely used as a benchmark for portfolio analysis. However, many portfolio studies still rely on conventional Value at Risk (VaR), which assumes normally distributed returns and may underestimate extreme losses, making it less effective in capturing tail risk. This study addresses this research gap by integrating Mean–Variance Optimization (MVO) with the Cornish–Fisher VaR approach, which incorporates skewness and kurtosis to accommodate non-normal return distributions. Daily adjusted closing price data of LQ45 stocks from January to December 2025 were obtained from Yahoo Finance, and logarithmic returns were calculated. Based on the highest Sharpe Ratios, BRPT, EXCL, and ANTM were selected as portfolio constituents. Correlation analysis shows low dependency among the selected stocks, supporting diversification, while normality tests confirm deviations from normality, justifying the use of Cornish–Fisher VaR. The optimal portfolio allocates 10.6% to BRPT, 65.5% to EXCL, and 23.9% to ANTM, producing an expected return of 65.7%, portfolio risk of 26.2%, and a Sharpe Ratio of 2.5, indicating strong risk-adjusted performance. Cornish–Fisher VaR estimates potential losses of 2.23%, 3.09%, and 5.30% at the 90%, 95%, and 99% confidence levels. These results demonstrate that combining MVO and Cornish–Fisher VaR offers a more robust framework for portfolio optimization in the Indonesian stock market.
Co-Authors Abdul Hamid Alamiyah, Syifa Syarifah Damaliana, Aviolla Terza Dwi Arman Prasetya Endah Setyowati Fahrudin, Tresna Maulana Hanif Assalmi, Fityan Heidy Arviani idhom, Mohammad Kusuma, Ade Lisanthoni, Angela Maulida H, Kartika Navsih, Muhammad Ghinan Pakpahan, Vera Febrianti Prabowo, Hendri Putri Pascha, Dea Putri, Deannisa Syafira Putri, Shafira Amanda Rahmawati, Adinda Aulia Regita Putri Permata Rusdianti Maulani Putri, Adinda Sa'diyah, Ilmatus Sari, Allan Ruhui Fatma Sari, Allan Ruhui Fatmah Selayanti, Nabilah Shindi Shella May Wara Sri Hidayati Suhartono Syaifullah Jauharis Saputra, Wahyu Syaifullah, Wahyu Taufik, Ikbar Athallah Trimono Trimono, Trimono