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All Journal JPM (Jurnal Pemberdayaan Masyarakat) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Sistem Informasi dan Aplikasi Rainstek : Jurnal Terapan Sains dan Teknologi Jurnal Minfo Polgan (JMP) BIMASAKTI
Ahsan, Moh
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Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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PERANCANGAN METODE HUMAN CENTERED DESIGN USER INTERFACE DAN USER EXPERIENCE PADA SISTEM HARGA KEBUTUHAN POKOK KABUPATEN MALANG Eka Wahyudi, Desantara; Budianto, Alexius Endy; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 1 (2025): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i1.12516

Abstract

The Ministry of Communication and Information with all its diverse society requires overlapping in terms of equalizing services, especially in terms of data information. The problem is caused by inaccurate data information so that the asset data used as the basis of the data affects the data information received which does not match the field conditions. The running of data processing so far is still based on technicalities that tend to be conventional so that a capable system is needed to accommodate the problem. By using a mixed method, this study uses Human Centered Design as the basis for designing an application design called SIHARKEPO (Basic Needs Price Information System). This study will later focus on two main problems, namely the SIHARKEPO user interface design process and the analysis of users of the Basic Needs Price Information System (SIHARKEPO) in providing data information to users. The results of this study are concrete applications that can be applied to the process of providing basic needs price information data services and make it easier for the public to obtain them.
PENERAPAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE PADA ANALISIS SENTIMEN NETIZEN DI TWITTER VOLLEY BALL INDONESIA Ginanjar, Wismo; Budianto, Alexius Endy; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12376

Abstract

Social media has become an integral part of modern society, offering a platform for public opinion expression. In Indonesia, volleyball is a very popular sport, and Volley Ball Indonesia is the main topic of discussion on social media, especially Twitter. This study aims to analyze the sentiment of netizen comments on the official Twitter account of Volley Ball Indonesia (@volleyball.indonesia) using the Naive Bayes method and Support Vector Machine (SVM). The data used amounted to 2,920 comments from 50 posts in the period of September 28, 2023 - May 10, 2024, focused on the U-23 and Senior Men's National Team matches. Naïve Bayes and SVM were chosen because both are effective methods in sentiment classification. Naïve Bayes uses a probabilistic approach, while SVM looks for the best hyperplane to separate data classes. The results of the study show that both methods can be used to analyze sentiment with a good level of accuracy. The test results on each training data and testing data with different presentations will provide different accuracy results. The test results of the Naive Bayes method obtained the highest accuracy value of 71% with a ratio of 70:30 and the Support Vector Machine obtained the highest accuracy value of 76% with a ratio of 80:20. So it can be concluded that the Support Vector Machine method gets a higher accuracy value than the Naive Bayes method.
OPTIMALISASI ANALISIS SENTIMEN FILM PADA YOUTUBE DENGAN ALGORITMA CHI-SQUARE PADA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE Wardana, Oky Kurnia; Budianto, Alexius Endy; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 1 (2025): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12377

Abstract

The management of correspondence and monthly reports in educational administration environments still faces efficiency issues due to workflows that do not align with user needs and insufficient attention to usability aspects. These conditions result in suboptimal performance and low effectiveness in the use of digital systems. This study aims to analyze the usability of the web-based SIRATU application using the User-Centered Design (UCD) approach in accordance with the ISO 9241-210 standard. The research method includes analysis of the context of use, user identification, interface design, and usability evaluation using the System Usability Scale (SUS). The study is limited to the primary users of the SIRATU application, namely administrative staff, school operators, and the head of the district education office, with a focus solely on usability aspects. The evaluation results show an increase in the average SUS score from 60.6 to 87.4, which falls into the excellent category. The contribution of this study lies in the application of a UCD methodological framework that has proven effective in improving the usability of the SIRATU application.
ANALISIS PERANCANGAN UI/UX PADA SISTEM TERINTEGRASI DATA PENDUDUK KABUPATEN MALANG (SI-CANTIK) MENGGUNAKAN METODE HUMAN CENTERED DESIGN Presetia, Ahmad Yudha; Budianto, Alexius Endy; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 1 (2025): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12517

Abstract

The management of correspondence and monthly reports in educational administration environments still faces efficiency issues due to workflows that do not align with user needs and insufficient attention to usability aspects. These conditions result in suboptimal performance and low effectiveness in the use of digital systems. This study aims to analyze the usability of the web-based SIRATU application using the User-Centered Design (UCD) approach in accordance with the ISO 9241-210 standard. The research method includes analysis of the context of use, user identification, interface design, and usability evaluation using the System Usability Scale (SUS). The study is limited to the primary users of the SIRATU application, namely administrative staff, school operators, and the head of the district education office, with a focus solely on usability aspects. The evaluation results show an increase in the average SUS score from 60.6 to 87.4, which falls into the excellent category. The contribution of this study lies in the application of a UCD methodological framework that has proven effective in improving the usability of the SIRATU application.
ANALISIS OPINI FILM PADA NETFLIX DENGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE MENGGUNAKAN SELEKSI FITUR CHI-SQUARE Riady, Rahman; Budianto, Alexius Endy; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.12982

Abstract

This research aims to analyse user opinions on films on the Netflix platform using the Naïve Bayes algorithm and Support Vector Machine. The focus of the research is to increase classification accuracy through feature selection using the Chi-square method. The data used is obtained through a web scraping process of user reviews on Google Play Store. Automatic labeling is supported by the Transformers library, resulting in 131 positive labels and 869 negative labels from 1000 reviews. The research stages include data crawling, automatic labeling using the Transformers library, pre-processing (case folding, tokenisation, stopword removal, normalisation, and stemming), weighting with the TF-IDF method, and testing model accuracy using data split ratios of 90:10, 80:20, and 70:30. The findings of the study indicate that the Support Vector Machine algorithm reached an accuracy rate of 92.5% using the 80:20 data split, whereas its Chi-square enhanced variant attained 91.5% accuracy on the same dataset. Meanwhile, the Naïve Bayes classifier recorded an accuracy of 82%, and its Chi-square integrated version yielded 79%. These results suggest that incorporating Chi-square did not enhance the predictive performance of either the Naïve Bayes or Support Vector Machine approaches in this research.
PENERAPAN ALGORITMA LOGISTIC REGRESSION UNTUK KLASIFIKASI PENYAKIT STROKE Amelia, Rachel Trivica; Nugraha, Danang Aditya; Ahsan, Moh
Jurnal Fakultas Teknologi Informasi Vol 8 No 2 (2026): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i2.13201

Abstract

Stroke is one of the leading causes of death worldwide, ranking after heart disease and cancer. Early detection of stroke risk is essential to enable faster and more accurate treatment. The purpose of this study is to apply the Logistic Regression algorithm to classify stroke cases based on several risk factors, including gender, age, hypertension, heart disease, marital status, occupation, residence type, average glucose level, body mass index (BMI), smoking status, and stroke status. The dataset used in this research was obtained from Kaggle and consists of 5,110 patient records. The research process involves several stages, including data cleaning, data transformation, and normalization using the Min-Max Scaler method, followed by splitting the data into training and testing sets with various proportions (90%-10%, 85%-15%, 80%-20%, 70%-30%, and 65%-35%). The evaluation was conducted using a Confusion Matrix with performance metrics such as accuracy, precision, recall, and F1-score. The analysis results show that the 90%-10% data split achieved the highest accuracy of 76.17%, with precision and recall values indicating that the model performs well in identifying non-stroke cases. However, performance on the minority class (stroke) remains relatively low, suggesting the need for improvement through data imbalance handling. Overall, the application of the Logistic Regression algorithm proved to be effective for initial stroke classification, although accuracy can still be improved through resampling techniques or advanced model optimization.
Co-Authors Abdul Aziz Amelia, Rachel Trivica Budianto, Alexius Endy Dianna Ratnawati Eka Wahyudi, Desantara Ginanjar, Wismo Lisdiana Lisdiana Mutaqin Akbar Nugraha, Danang Aditya Prasetyaningrum, Putri Taqwa Presetia, Ahmad Yudha Riady, Rahman Setuju Setyaningsih, Putry Wahyu Sigit Purnomo Syarif Hidayat, Yusron Wardana, Oky Kurnia Yuwono, Sapto Zamroni