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All Journal Jiko (Jurnal Informatika dan komputer) ComTech: Computer, Mathematics and Engineering Applications bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) G-Tech : Jurnal Teknologi Terapan Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings International Journal of Data Science, Engineering, and Analytics (IJDASEA) MATHunesa: Jurnal Ilmiah Matematika Indonesian Community Journal Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Journal of Information Systems and Technology Research Jurnal Informatika Teknologi dan Sains (Jinteks) STATISTIKA Jurnal Ilmiah Manajemen dan Akuntansi Teknologi : Jurnal Ilmiah Sistem Informasi
Amri Muhaimin
Universitas Pembangunan Nasional Veteran Jawa Timur
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering 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.
Forecasting Food Prices in East Java Using Stacking Ensemble Learning via K-MEANS Aviolla Terza Damaliana; Amri Muhaimin; Nabilah Selayanti; Shafira Amanda Putri; Muhammad Nasrudin
ComTech: Computer, Mathematics and Engineering Applications Vol. 17 No. 1 (2026): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v17i1.14218

Abstract

Food commodities are essential in developing countries such as Indonesia, and the government regulates food commodity prices in every province. However, price instability issues persist in certain provinces, creating challenges for effective policy control. Data science and statistical techniques play an important role in supporting the government’s efforts to monitor and manage food commodity prices. This study proposes the Stackelberg-K-Means method to predict the commodity price index in East Java. The proposed method is a collaborative framework that combines cluster analysis and stacking ensemble learning for time-series prediction. Cluster analysis is conducted first using Dynamic Time Warping as the distance measure, which is suitable for time-series data, resulting in two clusters for each commodity: rice, oil, and flour. The stacking model consists of base learners and a meta-learner. The base learner models include Ridge Regression, Random Forest, and Support Vector Regression, while the meta-learner uses Light Gradient Boosting. Parameter optimization is performed using grid search, and the proposed method is evaluated against AutoARIMA implemented in Python using both training and testing data. The results show that the proposed method outperforms the ARIMA model across all three error metrics: MAPE, MAE, and RMSE. For flour commodities, the scores are 0.042% versus 0.328%, 4.715 versus 37.57, and 6.34 versus 523.99, respectively. For rice commodities, the scores are 0.261% compared to 0.392%, 31.585 compared to 48.142, and 41.92 compared to 56.068. For oil commodities, the scores are 0.185% compared to 0.250%, 33.02 compared to 47.571, and 39.35 compared to 56.060.
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.
A Hybrid Neural Network-Time Series Regression Model for Intermittent Demand Forecasting Data Amri Muhaimin; Aviolla Terza Damaliana; Muhammad Nasrudin; Prismahardi Aji Riyantoko; Nabilah Selayanti; Shafira Amanda Putri
Journal of Advances in Information and Industrial Technology Vol. 7 No. 2 (2025): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v7i2.704

Abstract

Forecasting is a vital tool that helps us make informed decisions by predicting future events based on past data. For forecasts to be accurate, it is important that the data is reliable, complete, and consistent. Yet, the intermittent data is a unique data that is challenging to forecast. Intermittent data contains a characteristic that the data has a lot of long zeros in some periods. The zero value will influence the model to generate a forecasting model. This study aims to tackle those problems by applying a hybrid approach. We integrate the regression model and neural network to create a novel approach for forecasting intermittent data. The dataset used for this data is from Kaggle, sales at Walmart supermarket for one category only. The sales data always produce an intermittent demand pattern, because not every day are the items always sold to customers. This irregular pattern makes the data difficult to forecast using a naïve approach, such as the Croston method, exponential smoothing, and ARIMA. To evaluate the performance of our model, some metrics were calculated. We use mean squared error, root mean squared error, and root mean squared scaled error. The result shows that our proposed method outperforms the benchmark model, with an RMSSE of 0.98, which is the lowest compared to other benchmark models in the root mean squared scaled error value. This result shows promise as an exciting solution for overcoming the challenges posed by irregular data in future forecasting tasks.
Co-Authors Abdul Hamid Ade Kusuma Allan Ruhui Fatma Sari Allan Ruhui Fatmah Sari Andreas Nugroho Sihananto Andri Fauzan Adziima Angela Lisanthoni Aulia Rahmawati Aviolla Terza Aviolla Terza Damaliana Aviolla Terza Damaliana Aviolla Terza Damaliana Burhan Syarif Acarya Deannisa Syafira Putri Dwi Arman Prasetya Dwi Arman Prasetya Endah Setyowati Fanny Widya Cahyani Hauzan Hanifah Zahra Heidy Arviani Hendri Prabowo Hikmata Tartila Ilmatus Sa’diyah Jasmine Aulia Kanessa Jasmine Prisheila Az Zahra Setiyanto Kartika Maulida H Kartika Maulida Hindrayani Kartika Maulida Hindrayani Kartika Maulida Hindrayani Mareta Putri Wardhana Mohammad Idhom Muhammad Ghinan Navsih Muhammad Nasrudin Muhammad Nasrudin Nabilah Selayanti Nabilah Selayanti Nabilah Selayanti Naomi Dwi Anggraini Nine Alvariqati Varqa Ansori Prismahardi Aji Riyantoko Prismahardi Aji Riyantoko Reagen Westerdam Sean Jatindra Regita Putri Permata Rifdatun Ni’mah Rijal Apuila Khufi Sahat Renaldi. S Shafira Amanda Putri Shafira Amanda Putri Shafira Amanda Putri Shindi Shella May Wara Shindi Shella May Wara Siti Naia Hesti Rachmawati Sonya Audina Akbar Sugiarto, Sugiarto suhartono - Syifa Syarifah Alamiyah Tresna Maulana Fahrudin Tresna Maulana Fahrudin Trimono Trimono Trimono Trimono Trimono, Trimono Vera Febrianti Pakpahan Wahyu Syaifullah Wahyu Syaifullah Jauharis Saputra Wenny Maria Tampubolon Widiwurjani