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All Journal Jiko (Jurnal Informatika dan komputer) G-Tech : Jurnal Teknologi Terapan Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings International Journal of Data Science, Engineering, and Analytics (IJDASEA) Indonesian Community Journal Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi STATISTIKA Teknologi : Jurnal Ilmiah Sistem Informasi
Amri Muhaimin
Data Science Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Analisis Klaster Partitioning Around Medoids dengan Gower Distance untuk Rekomendasi Indekos (Studi Kasus: Indekos di Sekitar Kampus UPNVJT) Sahat Renaldi. S; Dwi Arman Prasetya; Amri Muhaimin
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4898

Abstract

In choosing a boarding house, many criteria are considered by prospective tenants. This situation requires an analytical approach that can assist individuals in finding a boarding house while taking into account their preferences. This study aims to develop a recommendation system using K-Prototype and Partitioning Around Medoids (PAM) with Gower Distance. The PAM method with Gower Distance performed better, achieving a Silhouette index of 0.45, and successfully clustered boarding houses into two categories: standard and exclusive. The recommendation system is based on Gower distance similarity. User Acceptance Test (UAT) results showed a score of 70%, indicating that the system is generally well-received and acceptable to users.
Intermittent Data Forecasting using Kernel Support Vector Regression Amri Muhaimin; Endah Setyowati; Kartika Maulida H; Allan Ruhui Fatma Sari
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4105

Abstract

Forecasting involves making future estimates. Forecasting methods are commonly employed to predict stock prices, monetary distribution, and weather conditions. To generate accurate forecasts, it is crucial that the data used is consistent, comprehensive, and unchanging. Some data can be readily predicted, while some poses a considerable challenge. An illustration of this is found in discontinuous data, which is notably hard to forecast. Discontinuous data is marked by frequent instances of zero values due to sporadic events. For instance, when tracking the sales of aircraft or other products, sales do not transpire daily, causing recorded data to often register as zero. Various techniques have been explored to handle this kind of data. In this particular study, the chosen method is support vector regression. This method is capable of predicting discontinuous data with a quality level of 1.004, which is lower than traditional approaches like exponential smoothing.
Sentiment Analysis in Social Media: Case Study in Indonesia Amri Muhaimin; Tresna Maulana Fahrudin; Syifa Syarifah Alamiyah; Heidy Arviani; Ade Kusuma; Allan Ruhui Fatmah Sari; Angela Lisanthoni
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4106

Abstract

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. There have been many government efforts in implementing programs to reduce stunting rates. However, not everything runs optimally. Rapid technological developments and freedom of expression in the internet world produce review text data that can be analyzed for evaluation. This study aims to analyze 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 (LDA). 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 'anak', 'turun', 'angka', 'cegah' and 'gizi' are among the words that often appear on the topic of stunting.
Forecasting The Number of Traffic Accidents in Purbalingga Regency on 2023 Using Time Series Model Trimono; Amri Muhaimin; Nabilah Selayanti
Nusantara Science and Technology Proceedings 8th International Seminar of Research Month 2023
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2024.4168

Abstract

Accident data from Satlantas Purbalingga Regency shows that in 2022 there is an increase in the number of traffic accidents in the Purbalingga Regency. In the future, the impact of accidents is predicted to be bigger so it is necessary to forecasting. Forecasting is one of the most important elements in decision making, because effective or not a decision generally depends on several factors that can not be seen at the time the decision was taken. In this time study the possible time series model is ARMA (2,2), ARMA (2,1), ARMA (1,2), ARMA (1,1), AR (2), AR (1), MA (2), MA (1). However, after testing, the model used is ARMA (1,1). This model is used because it meets all the assumption requirements that are parameter significant, residual independent test, residual normality test, and the smallest Mean Square Error value. According to data forecasting results the highest number of crashes existed in January of 97 accidents and the lowest in December amounted to 93 accidents, So the necessary action from the relevant agencies to cope with the increasing number of traffic accidents in the Purbalingga Regency.
Stock Price Modeling with Geometric Brownian Motion and Value with Risk PT Ciputra Development TBK Amri Muhaimin; Trimono Trimono
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3329

Abstract

Financial sector investment is an activity that attracts a lot of public interest. One of them is investing funds in purchasing the company’s shares. Profit received from stock investment activity can be seen from the value of stock returns. While, if the previous stock returns to Normal distribution, the future stock price can be predicted by Geometric Brownian Motion Method. Based on the stock price prediction, can also be measured an estimated value of the investment risk. The result of data processing shows that the stock price prediction of PT. Ciputra Development Tbk period December 1, 2016, until January 31, 2017, has very good accuracy, based on the value of MAPE 1.98191%. Further, the Value Risk Method of Monte Carlo Simulation with ? = 5% significance level was used to measure the share investment risk of PT.Ciputra Development Tbk. Thus, this method is only useful if it can be used to predict accurately. Therefore, backtesting is needed. Based on the processing obtained data, backtesting generates the value of violation ratio at 0, it means that at significance level ? = 5%, the Value at Risk Method of Monte Carlo Simulation can be used at all levels of probability violation.
A Simple Data Sentiment Analysis using Bjorka phenomenon on Twitter Prismahardi Aji Riyantoko; Amri Muhaimin
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3353

Abstract

Social media is one of the means used by netizens to access, share and discuss the latest and hottest news issues. Twitter as one of the social media is a platform that in real-time is often chosen to communicate that matter. Through sentiment analysis with the text method mining on Twitter, we can understand how people describe and express their perceptions of obesity both positively and negatively nor neutral. This analysis is important to see the extent to which social media such as Twitter is used today. Those are one of the instruments for disseminating information data security in Indonesia. Research objectives for identifying sentiment analysis on related Twitter the Bjorka phenomenon in Indonesia using the text mining method. The type of research is cross-sectional. This research plan was chosen because of the data taken from Twitter in the last four-month time series (June 2022 - October 2022). The result of web scraping on Twitter is 998 Indonesian tweets. Taking data using the Twitter Scraping extension pack and analyzing using Python 3.7.2. Based on the results of sentiment analysis tweets got a neutral sentiment of 744 (75%) tweets, followed by negative sentiment of as much as 175 (18%) tweets and positive sentiment by the number 75 (8%) of a total of 994 tweets. The conclusion was presented the modelling in based on the topic, and we got three topic most relevant terms for topic 0, 1, or 2 with 35,3%, 33%, 31,7% of tokens, respectively.
Pemanfaatan Digitalisasi Praktik Baik Guru Bahasa Indonesia Tingkat SMP di Surabaya untuk Meningkatkan Kompetensi Pedagogik Ilmatus Sa'diyah; Abdul Hamid; Widiwurjani Widiwurjani; Amri Muhaimin
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi pedagogik guru Bahasa Indonesia SMP di Kota Surabaya melalui pemanfaatan digitalisasi praktik baik. Permasalahan yang dihadapi adalah belum optimalnya dokumentasi dan diseminasi praktik baik yang dihasilkan guru dalam forum MGMP, sehingga potensi inovasi pembelajaran belum tersebar secara luas. Adapun tujuan dari kegiatan pengabdian ini yaitu meningkatkan dan mengembangkan kompetensi pedagogik guru melalui digitalisasi praktik baik dari hasil kegiatan belajar mengajar di kelas.  Metode pelaksanaan pada kegiatan pengabdian kepada masyarakat ini dilakukan melalui lima tahapan, yaitu persiapan, pelatihan, penerapan teknologi, pendampingan dan evaluasi, serta keberlanjutan program. Sekitar 50 guru mengikuti kegiatan ini yang mencakup pretest, pelatihan literasi digital, praktik unggah karya pada platform https://guru.thalibulilmi.com/, serta posttest. Hasil kegiatan menunjukkan peningkatan signifikan pada kompetensi pedagogik guru, ditandai dengan nilai posttest yang tinggi serta respon positif dari peserta, di mana lebih dari 85% menyatakan kegiatan ini sesuai kebutuhan. Oleh karena itu, digitalisasi praktik baik cukup efektif sebagai sarana kolaboratif dalam pengembangan profesionalisme antara Guru Bahasa Indonesia Tingkat SMP di Surabaya.
CLASSIFICATION OF JAVANESE NGLEGENA SCRIPT USING COMPLEXVALUED NEURAL NETWORK Adinda Aulia Rahmawati; Amri Muhaimin; Dwi Arman Prasetya
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 1 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7808

Abstract

Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.
Sistem Rekomendasi Menu Kantin Menggunakan Lifespan-Aware Association Rule Mining Dengan Hybrid Apriori Dan FP-Growth Muhammad Ghinan Navsih; Amri Muhaimin; Shindi Shella May Wara
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 16 No 1 (2026): January
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.
Prediksi Viralitas Tweet Berbahasa Indonesia Menggunakan IndoBERTweet, RoBERTa, dan Multi-Layer Perceptron untuk Optimalisasi Strategi Pemasaran Digital Deannisa Syafira Putri; Amri Muhaimin; Mohammad Idhom
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.
Co-Authors Abdul Hamid Ade Kusuma Adinda Aulia Rahmawati Allan Ruhui Fatma Sari Allan Ruhui Fatmah Sari Andreas Nugroho Sihananto Angela Lisanthoni Aviolla Terza Burhan Syarif Acarya Deannisa Syafira Putri Dwi Arman Prasetya Dwi Arman Prasetya Endah Setyowati Heidy Arviani Hendri Prabowo Ilmatus Sa’diyah Kartika Maulida H Kartika Maulida Hindrayani Mohammad Idhom Muhammad Ghinan Navsih Nabilah Selayanti Nine Alvariqati Varqa Ansori Prismahardi Aji Riyantoko Regita Putri Permata Rifdatun Ni’mah Sahat Renaldi. S Shindi Shella May Wara suhartono - Syifa Syarifah Alamiyah Tresna Maulana Fahrudin Tresna Maulana Fahrudin Trimono, Trimono Widiwurjani Widiwurjani