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All Journal Jurnal Mahasiswa Fakultas Teknologi Informasi SMATIKA JPM (Jurnal Pemberdayaan Masyarakat) MBR (Management and Business Review) SMARTICS Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Sistem Informasi dan Aplikasi Kurawal - Jurnal Teknologi, Informasi dan Industri Kumawula: Jurnal Pengabdian Kepada Masyarakat JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Dharma Bhakti Ekuitas Rainstek : Jurnal Terapan Sains dan Teknologi BIMASAKTI Smatika Jurnal : STIKI Informatika Jurnal
Budianto, Alexius Endy
Universitas PGRI Kanjuruhan Malang
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 Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Implementasi E-Commerce untuk Pengembangan Market Produk Kewirausahaan Mahasiswa Budianto, Alexius Endy; Iswahyudi, Didik; Dianawati, Eris
Jurnal Pemberdayaan Masyarakat Vol 5 No 2 (2020): November
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/jpm.v5i2.5010

Abstract

The development of student entrepreneurship at the University of Kanjuruhan Malang is starting to show results. This is very supportive of government programs in their development in line with increasing economic growth. Furthermore, it has an impact on student entrepreneurial actors to be more creative and innovative in effective marketing strategic planning. Most entrepreneurial students have difficulty developing because they do not understand how to market a product effectively, display product packaging attractively so that it has high selling value and manages their business well. On this occasion, we aim to provide solutions through the Entrepreneurship Development Program of the University of Kanjuruhan Malang which collaborates with micro, small and medium enterprises (MSMEs) in Malang City and Regency, in the form of a digital marketing strategy, namely e-commerce. The method we use in this activity is conducting workshops by forming student groups according to their target products and market reach, and providing assistance to these student groups. The investment that we provide is in the form of e-commerce applications. The results of the investment show a significant change in marketing among entrepreneurial students at Kanjuruhan University of Malang.
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.
PENERAPAN ALGORITMA C4.5 PADA ULASAN APLIKASI SHOPEE DI GOOGLE PLAY STORE Andriasih, Indra Fitri; Budianto, Alexius Endy; Walidaroyani, Ainia
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.v8i2.12763

Abstract

Shopee, as one of the most popular e-commerce platforms in Indonesia, receives numerous user reviews on the Google Play Store. These reviews contain valuable information that can be leveraged to evaluate the quality of the application’s services. This study aims to classify user sentiment using the C4.5 algorithm to assist developers in better understanding user perceptions. The data were collected from the Google Play Store and processed through several stages, including preprocessing (case folding, stopword removal, stemming, word normalization, and sentiment labeling), data transformation using the TF-IDF method, and splitting the dataset into training and testing sets. The C4.5 algorithm was implemented using the DecisionTreeClassifier model with entropy as the criterion. The results indicate that the classification model achieved an accuracy of 83.25% on the test data. The model demonstrated strong performance in classifying positive sentiment, while the classification of negative and neutral sentiments was less optimal due to class imbalance. Therefore, the C4.5 algorithm proves to be effective in classifying user review sentiment, particularly in identifying positive sentiment. These findings can serve as valuable input for Shopee's developers to improve their services based on user feedback.
PENERAPAN DATA MINING UNTUK MENGKLASIFIKASI PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Jesika, Amelia; Budianto, Alexius Endy; Nugraha, Danang Aditya
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.v8i1.12826

Abstract

The Family Hope Program (PKH) is a governmental initiative in Indonesia designed to decrease poverty and improve the welfare of families. However, the process of identifying eligible families frequently encounters difficulties. To address this, the study applies data mining techniques with the Support Vector Machine (SVM) method to classify prospective PKH recipients in Bangka Leleng Village. The research utilizes 1,039 data samples of recipients from 2019 to 2023, based on five key attributes: age, income, number of dependents, occupation, and home ownership status. Data processing was conducted using Python in the Google Colab environment. The research workflow involved data collection, preprocessing, splitting data for training and testing, analysis, and evaluation using a Confusion Matrix. The test results indicated that the SVM method is highly effective in classifying PKH recipients, achieving an accuracy rate of up to 96%. This optimal accuracy was obtained by employing the RBF kernel, which demonstrated superior performance compared to other kernels. It is anticipated that this research will provide a more efficient and transparent method for determining aid recipients, leading to a more precise distribution of assistance.
ANALISIS OPINI FILM PADA NETFLIX DENGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE MENGGUNAKAN SELEKSI FITUR CHI-SQUARE Rahman Riady; Alexius Endy Budianto; Moh Ahsan
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.
PERBANDINGAN METODE ARTIFICIAL NEURAL NETWORK, DAN RANDOM FOREST PADA KLASIFIKASI TINGKAT OBESITAS Agung Indra; Amak Yunus; Alexius Endy Budianto
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.v8i2.13171

Abstract

Classification of obesity levels is an important step in supporting efforts to tackle the increasing prevalence of obesity. This study aims to compare the performance of machine learning methods, namely Artificial Neural Network (ANN) and Random Forest (RF), in classifying obesity levels based on a predetermined dataset. The research method involved data preprocessing and model training with varying proportions of training and testing data (70:30, 80:20, and 90:10). The results showed that Random Forest provided higher accuracy than Artificial Neural Network. In testing with 70% training data and 30% testing data, ANN produced an accuracy of 88.20% while RF reached 97.28%. With a training data proportion of 80% and testing data of 20%, the accuracy of ANN increased to 88.76%, while RF produced 97.37%. With a training data proportion of 90% and testing data of 10%, ANN achieved the highest accuracy of 91.39%, but it was still lower than RF, which reached 95.69%. Based on these results, it can be concluded that the Random Forest algorithm shows more optimal performance than Artificial Neural Network in obesity level classification.
Co-Authors Abdul Aziz Abdul Aziz Agung Indra Ahsan, Moh Amak Yunus Andriasih, Indra Fitri Antonius Yanto Haki Aprimavista, Laily Irana Bagus Wira Cahyono, Achmad Febrian Dwi Didik Iswahyudi Ediono Eka Wahyudi, Desantara Elmi, Elmi Tiodata Eris Dianawati Eris Dianawati Fransiskus Heri Ginanjar, Wismo Harianto, Wahyudi Heri Santoso Ibnu Arivin Ilmi, Ahmad Rizal Iswahyudi, Didik Ita Purnawati Jesika, Amelia M Haris Adiansyah Meviana, Ika Moh Ahsan Mohammad Ahsan Mutaqin Akbar Naja, M Muttaqi Saifun Nataliya, Sukma Nugraha, Danang Aditya Nur Hidayah Prasetyaningrum, Putri Taqwa Presetia, Ahmad Yudha Rahman Riady Sartika Wiguna, Anggri Setyaningsih, Putry Wahyu Silih Windanu, Leonardus Sofyan Haidy Saputra Sri Wilujeng Syahminan . Syahminan Syahminan Wahyudi Harianto Walidaroyani, Ainia Wardana, Oky Kurnia Yuli Ifana Sari, Yuli Ifana Yusriel Ardian Yusriel Ardian