Article Per Year (5 Year)
p-Index From 2021 - 2026
3.625
P-Index
This Author published in this journals
All Journal Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Kursor TEKNOLOGI INFORMASI Sistemasi: Jurnal Sistem Informasi SMARTICS Journal Jurnal TAM (Technology Acceptance Model) INOVTEK Polbeng - Seri Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Teknologi Terapan J-Dinamika: Jurnal Pengabdian Kepada Masyarakat EDUMATIC: Jurnal Pendidikan Informatika MULTINETICS Jurnal Teknologi Informasi dan Multimedia Jurnal Pengabdian Polinema Kepada Masyarakat Buguh: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Paradigma Jurnal ELTEK Jurnal Teknik Ilmu dan Aplikasi Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Informatika Polinema (JIP) Jurnal TAM (Technology Acceptance Model) Jurnal Komtika (Komputasi dan Informatika)
Imam Fahrur Rozi
Politeknik Negeri Malang
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences

Published : 40 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 40 Documents
Search

 1   2   3   4 
Integrasi Sistem Informasi Geografis dan Sistem Informasi Manajemen Keanggotaan untuk Meningkatkan Aksesibilitas Layanan Kesehatan pada Ikatan Dokter Indonesia (IDI) Cabang Malang Raya Rozi, Imam Fahrur; Ariyanto, Rudy; Arianto, Rakhmat; Hapsari, Ratih Indri; Ananta, Ahmadi Yuli; Rohadi, Erfan; Widito, Sasmojo; Zakaria, Arief Syukron; Budiarti, Arry; Saputra, Zainal Ulu Prima; Irawan, Ferry Buyung Bakhtiar; Sholiha, Afifah
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 10 No 1 (2025): April
Publisher : Politeknik Negeri Jember

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

Abstract

The Indonesian Medical Association (IDI) Chapter Malang Raya, which covers area of Malang City, Batu City, and Malang Regency, faces challenges in managing doctor membership data and presenting information related to customer services and public services. To overcome these obstacles, a website-based information system was developed that integrates the Membership Management Information System with the Geographic Information System (GIS). The Membership Management Information System facilitates efficient management data of physician member of IDI chapter Malang Raya, including status of membership, competence of medical doctors, speciality, and subspeciality. Whereas GIS system serves to map the location of doctor practices. Integration these two systems making it easier for people to find the nearest health services. The system was developed using the waterfall methodology, which involves the stages of requirements analysis, design, implementation, testing and maintenance. The result is a platform that can improve IDI's internal efficiency and make it easier for people to access health services. This system has the potential to be further developed with the addition of security features and functionality such as real-time monitoring and mobile application integration, thus supporting more responsive and integrated health services
IMPLEMENTASI SUPPORT VECTOR MACHINE PADA ANALISA SENTIMEN TWITTER BERDASARKAN WAKTU Faisal Rahutomo; Imam Fahrur Rozi; Haris Setiyono
Jurnal TAM (Technology Acceptance Model) Vol 10, No 2 (2019): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v10i2.744

Abstract

Sentiment analysis is one branch of science from data mining that aims to analyze, understand, process, and extract textual data in the form of opinions on entities such as products, services, organizations, individuals, and certain topics. In determining positive, negative or neutral categories, a public response on twitter can be done manually by reading each tweet. This certainly requires a lot of time and takes a lot of energy. In this study using the Support Vector Machine classification algorithm to classify tweet data into positive, negative or neutral sentiments. Analysis is carried out based on a certain time span, because each time can have a different topic of discussion and from the results of these data can be seen the development of sentiment trends and can be seen how the public response to a particular topic. The tweet data is obtained by crawling periodically with the target keywords of the names of candidates and vice president in the 2019 election. The dataset used in this study uses 600 tweets. In testing the classification using k-fold cross validation by dividing into 10 data parts, average value of 66% accuracy, 67% precision and 66% recall.
IMPLEMENTASI GAMIFIKASI DALAM PLATFORM PEMBELAJARAN PEMROGRAMAN BAHASA JAVA BERBASIS WEBSITE Saputra, Pramana Yoga; Yunianto, Dika Rizky; Rozi, Imam Fahrur; Nurhasan, Usman; Wijanarko, Eko Setio; Al Huda, Muhammad Iqbaluddin
Jurnal Teknologi Terapan Vol 10, No 2 (2024): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v10i2.637

Abstract

The Industry 4.0 era is characterized by a revolution involving automation and artificial intelligence, distinguishing it from previous generations. This automation is driven by machine learning, a system that enables machines to learn from experience and data. Machine learning requires strong programming skills, which are developed through effective learning processes. However, many students encounter difficulties in learning programming, particularly during the pandemic, which has hindered face-to-face instruction. These difficulties include a lack of motivation and understanding in problem-solving. To address these issues, researchers conducted a study by developing a web-based programming learning platform that implements Gamification learning methods. This technology-enhanced learning platform is specifically designed for the Java programming language and aims to enhance student motivation and understanding through online learning modules and practical exercises. The results of this study demonstrate that the use of the learning platform has a significant positive impact, as evidenced by Wilcoxon test results. The testing results show that 20 users of the system experienced improved learning outcomes. The Asymp.Sig (2-tailed) value of 0.000 indicates that there is a significant effect of using the learning platform on the Java programming learning outcomes for users..
Comparison of Feature Extraction in Support Vector Machine (SVM) Based Sentiment Analysis System Rozi, Imam Fahrur; Maulidia, Irma; Hani’ah, Mamluatul; Arianto, Rakhmat; Yunianto, Dika Rizky; Ananta, Ahmadi Yuli
Jurnal Ilmiah Kursor Vol. 13 No. 1 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i1.417

Abstract

Sentiment analysis plays a crucial role in natural language processing by identifying and categorizing opinions or emotions conveyed in textual data. It is widely applied across diverse fields such as product review analysis, social media monitoring, and market research. To enhance the accuracy and reliability of sentiment classification, various methods and feature extraction techniques have been explored. This study investigates the use of Support Vector Machine (SVM) for sentiment analysis, comparing three feature extraction techniques: Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and Word2Vec. Our findings indicate that SVM performs effectively with all three feature extraction methods, with TF-IDF yielding the highest accuracy at 0.79. Although the BoW method showed competitive results, it slightly trailed TF-IDF in k-fold validation. Word2Vec, however, exhibited the lowest performance, achieving a maximum accuracy of 0.69. A comparative analysis of accuracy, precision, recall, and F1-score highlight the superiority of TF-IDF in delivering consistent and accurate results. Further statistical analysis using ANOVA revealed no significant differences between the models across any of the evaluation metrics. Additionally, the evaluation was conducted under several scenarios, including tests on balanced and imbalanced datasets, varying dataset sizes, and different CCC parameter values for SVM. These scenarios provided deeper insights into the factors influencing the system's performance, reinforcing that TF-IDF combined with SVM remains the most effective approach in this study.
Analisis Performa Metode Extreme Learning Machine dan Multiple Linear Regression dalam Prediksi Produksi Gula Ananta, Ahmadi Yuli; Ariyanto, Rudy; Rozi, Imam Fahrur; Arianto, Rakhmat
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29626

Abstract

Sugar is a crucial commodity in Indonesia, with demand increasing annually. Variations in sugar production require accurate prediction strategies for industrial planning. This study aims to analyze the performance of the Extreme Learning Machine (ELM) and Multiple Linear Regression (MLR) methods in predicting sugar production. This research employs a quantitative experimental approach, with sugar production data during the 2020-2023 milling period as the research subject. Data collection techniques involve observation and documentation, while data analysis techniques utilize Mean Absolute Percentage Error (MAPE) and 10-Fold Cross-Validation to measure model accuracy. The results indicate that ELM has a lower error rate (MAPE 16.06%) compared to MLR (MAPE 27.90%), making it more effective in capturing complex sugar production patterns. Implementing this model in a web-based system also enables more efficient production monitoring. The ELM method proves to be superior in predicting sugar production and can be integrated into industrial systems to support data-driven decision-making. Future research can explore other predictive models, such as deep learning, and consider external factors like weather and soil conditions to enhance accuracy.
Sistem Pakar Diagnosa Hama Penyakit Tanaman Kentang Dengan Metode Forward Chaining Rahman, Muhammad Arif; Rozi, Imam Fahrur; Hani'ah, Mamluatul
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11128

Abstract

Potatoes (Solanum tuberosum L.) are a priority vegetable crop due to their high domestic trade value and export potential. Potatoes are used for various purposes, both as a vegetable and as a carbohydrate substitute. In addition to being used as a vegetable, potatoes are also utilized as raw materials in the food industry, such as chips, potato flour, and potato starch. Due to the relatively low temperature requirement (20-22°C) for tuber formation, potato cultivation areas in Indonesia are generally located in mountainous regions. One of the potato commodity centers is in the city of Batu, particularly in the Bumiaji District. According to vegetable crop potential data from the Batu City extension program in 2022, the area planted with potatoes is 485.2 hectares with a production potential of 968 tons. Since potato plants are more susceptible to pests and diseases, substandard maintenance can lead to low harvest yields, poor sales, and even crop failure. This issue has led to the development of an application for diagnosing potato pests. The expert system uses forward chaining methods and is web-based. The expert system processes facts answered by users of the potato application, diagnoses the symptoms present, and generates diagnostic results in the form of solutions for the diagnosed potato plant diseases or pests. With the availability of an expert system application for diagnosing potato plant diseases and pests, the limitation of expert manpower is no longer a hindrance for potato farmers. Recommendations and information regarding potato diseases and pests can be obtained online without the need to consult a specialist.
Support Vector Machine with FastText Word Embedding for Hate Speech Aspect Categorization Mardiana, Aida Milati; Rozi, Imam Fahrur; Arianto, Rudy
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.5127

Abstract

Freedom of expression on Twitter often leads to issues such as hate speech, which may include provocation, incitement, or insults based on race, religion, gender, and other aspects. To address this issue, machine learning techniques can be applied to automatically classify hate speech. Therefore, this study aims to implement a machine learning–based approach for automatic hate speech aspect classification and to evaluate the accuracy of the obtained results. Support Vector Machine is used as the classifier method, with FastText as the word embedding method in the categorization process of hate speech aspects. The categorized aspects include abusive, individual, group, religion, race, physical, gender and other. The dataset used in this research is a collection of Indonesian tweets from Kaggle, which have been classified into each aspect. This study also tested combinations of preprocessing methods, namely filtering with stemming and the FastText pre-trained model. From the test results of the application of the Support Vector Machine method with FastText word embedding, with parameters C value = 1.0, gamma = 1.0 and RBF kernel and the ratio between training data and testing data is 90:10, the best results were obtained accuracy 98%, precision 98%, recall 98% and F1-Score 97% on Physical and Gender aspects. In addition, this study also tested if it did not use fasttext word embedding and the accuracy results showed 84%, precision 74%, recall 86% and F1 Score 79% in the abbusive aspect.
Inovasi Pengelolaan Dana Sosial Keagamaan melalui Aplikasi Mobile ZIS: Studi Kasus Masjid Raya An-Nur: Innovation in Managing Religious Social Funds through a Mobile ZIS Application: A Case Study at Masjid Raya An-Nur Rozi, Imam Fahrur; Yunianto, Dika Rizky; Yunhasnawa, Yoppy; Purnomo, Fadjar; Amalia, Astrifidha Rahma; Arifin, Muh. Syamsul; Sholeh, Moch.; Viyus, Vinan; Hakim, Muhammad Ilham El
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 12 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i12.10791

Abstract

The management of zakat, infaq, and sadaqah (ZIS) funds in many mosques is still carried out conventionally, leading to inefficiencies in collection, reporting, and distribution, as well as limited transparency and accountability. Masjid Raya An-Nur at Politeknik Negeri Malang, as a religious activity center, plays a strategic role in managing community funds. However, its manual system has hindered timely financial recording and reporting. The main challenges include limited access for donors to contribute online, slow verification processes, and a lack of transparency that may reduce public trust. To address these issues, this community service program developed and implemented an Android-based mobile application for ZIS management. The methodology involved a needs analysis through interviews and observations, system design utilizing REST API architecture with a Laravel back-end and MySQL database, and user training for both administrators and donors. The results indicate that the application successfully facilitates online donations, provides real-time reporting, and enhances donor participation through transparency features and digital reminders. Furthermore, the payment gateway integration enables more flexible and secure payment methods. Overall, the system has improved the efficiency, transparency, and accountability of ZIS management at Masjid Raya An-Nur, strengthened donor trust, and contributed to the digital transformation of religious social fund governance.
Implementasi Website Profil Sebagai Strategi Peningkatan Penjualan Di Toko Kue Macaron Tuffero Ahmadi Yuli Ananta; Rudy Ariyanto; Rakhmat Arianto; Imam Fahrur Rozi; Erfan Rohadi; Farida Ulfa
KOMUNITA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 5 No 1 (2026): Februari
Publisher : PELITA NUSA TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60004/komunita.v5i1.394

Abstract

This community service activity was carried out with the aim of enhancing the competitiveness and brand image of Macaron Tuffero through the development of a Profile Website as an effective and sustainable digital promotion medium. The partner of this program is the Macaron Tuffero SME, which operates in the production of cakes and desserts, located in Dau District, Malang Regency. The activity involved the business owner and staff as primary participants who collaborated actively with the community service team from Politeknik Negeri Malang. The main problem faced by the partner was the limited use of digital promotion media and the absence of an official platform to professionally present the business profile. To address this issue, the activity was implemented through five main stages: needs analysis, website design and architecture planning, system development and implementation, testing, and website usage training. The website was developed using the WordPress platform with the WooCommerce plugin to facilitate product catalogs and online ordering features. The results show that the developed website functions properly, consistently presents the business’s visual identity, and integrates with various social media and marketplace platforms. The website also provides convenience for the business owner to independently update content. Overall, this community service program successfully delivered a practical digital solution that contributes to marketing capacity enhancement and supports digital transformation among SMEs in the technology-based economy era.
Pengembangan Deteksi Pesan Spam pada Website Inti Everspring Indonesia Menggunakan Algoritma Support Vector Machine Akbar, Syafaat; Hani'ah, Mamluatul; Rozi, Imam Fahrur
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.872

Abstract

The development of information technology has driven the growth of email-based communica-tion in business environments, including at Inti Everspring Indonesia. However, the high volume of incoming emails increases the potential for spam messages that may disrupt work effectiveness and data security. This study develops a spam detection system on the company’s website by ap-plying the Support Vector Machine (SVM) algorithm. SVM was selected because of its ability to perform text classification efficiently. The dataset used in this research comes from the company’s internal emails, consisting of labeled spam and non-spam messages. Since the dataset is imbal-anced, an oversampling process was applied, followed by text preprocessing steps including case folding, tokenization, removal of stop words, symbols, numbers, and stemming. The model was then trained using the SVM algorithm, and its performance was evaluated using several metrics: accuracy, recall, precision, and F1-score. Based on the experiments, the SVM-based spam detec-tion model achieved 100% precision, 100% recall, and a 100% F1-score. To validate the reliabil-ity of the algorithm, SVM performance was compared with BERT and Naïve Bayes. BERT achieved 96% accuracy, and Naïve Bayes achieved 97% accuracy. These results indicate that SVM is capable of classifying messages accurately, and SVM outperforms both algorithms.
Co-Authors Abdul Muhsyi Agung Nugroho Ahmad Adil Faruqi Akbar, Syafaat Al Huda, Muhammad Iqbaluddin Ali Rahman Wibisana, Hafid Amalia, Astrifidha Rahma Ananta, Ahmadi Yuli Angga Aditya Indra Wiratmaka Anggraini, Serly Anita Ivianti Annisa Taufika Firdausi Annisa Taufika Firdausi Arief Prasetyo Arifin, Muh. Syamsul Atiqah Nurul Asri Batubulan, Kadek Suarjuna Budiarti, Arry Budiarti, Mahanani Nur Bulan, Novita Putri Dianti, Amelia Dika Rizky Yunianto Dimas Firman AL-Hafiidh Donavan, Khasadika Dwi Puspitasari Ekojono Ekojono Ekojono, Ekojono Elok Nur Hamdana Endah Septa Sintiya Erfan Rohadi Fahmy Ainun Nazilla Faisal Rahutomo Faishal Rahutomo Farida Ulfa Faruqi, Ahmad Adil Gaghana, Geo Alfriza Hakim, Muhammad Ilham El Hapsari, Ratih Indri Haris Setiyono Ika Kusumaning Putri Indra Wiratmaka, Angga Aditya Iqbal Alfahmi, Muhammad Balya Irawan, Ferry Buyung Bakhtiar Irvan Wahyu Nurdian Islamiyah, Khalimatul Ivianti, Anita Khalimatul Islamiyah Khansa, M. Roid Billy Khasadika Donavan Mahanani Nur Budiarti Mamluatul Hani’ah Mardiana, Aida Milati Maulidia, Irma Millenia Rusbandi Moch. Sholeh, Moch. Mochamad Panggih Nirwanto Mufidah, Nursita Al Muhammad Afif Hendrawan Muhammad Alfahmi Nazilla, Fahmy Ainun Nirwanto, Mochamad Panggih Novia Puspitasari Nugraeni, Arin Kistia Nur Khozin Nurdian, Irvan Wahyu Nursita Al Mufidah Nurudin Santoso Odhitya Desta Odhitya Desta Triswidrananta Odhitya Desta Triswidrananta Pangestu Nur Mirzha Pramana Yoga Saputra Pramudhita, Agung Nugroho Purnomo, Fadjar Rahmad, Cahya Rahmadhany, Tahta Reza Rahman, Muhammad Arif Rakhmat Arianto Ridwan Rismanto Rokhman, Syaiful Rosa Andrie Asmara Rudy Arianto, Rudy Rudy Ariyanto Rudy Ariyanto Rusbandi, Millenia Santoso, Nurudin Saputra, Zainal Ulu Prima Sholiha, Afifah Syaiful Rokhman Tahta Reza Rahmadhany Taufika Firdausi, Annisa Thalia Amira Rifda Usman Nurhasan Vipkas Al Hadid Firdaus Vivi Nur Wijayaningrum Vivin Ayu Lestari Viyus, Vinan Wibowo, Rahmat Catur Widito, Sasmojo Wijanarko, Eko Setio Yan Watequlis Syaifudin Yogi Kurniawan Yoppy Yunhasnawa Yuri Ariyanto Yushintia Pramitarini Zakaria, Arief Syukron Zanuar Hanif Rachmat Adi