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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Informatika Universitas Pamulang Zonasi: Jurnal Sistem Informasi Jurnal Informatika Ekonomi Bisnis JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis
Nazruddin Safaat
Teknik Informatika UIN SUSKA Riau
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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User Interface untuk Tampilan Website Berita Mobile bagi Penyandang Rabun Dekat Tania, Windy; Candra, Reski Mai; Safaat, Nazruddin; Affandes, Muhammad
Techno.Com Vol. 22 No. 1 (2023): Februari 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i1.7226

Abstract

Saat ini banyak website yang disajikan dalam bentuk mobile , yang menyediakan berbagai informasi kepada pengguna, termasuk diantaranya website berita mobile . Namun, bagi pengguna yang mengalami rabun dekat kesulitan merasa dalam membaca. Peneliti sudah melakukan wawancara terhadap 3 orang yang mengalami rabun dekat dengan rentang usia 30-60 tahun dengan jarak 25-30 cm, dari hasil wawancara menyatakan bahwa orang yang mengalami rabun dekat kesulitan dalam membaca karena font nya, ukurannya, tidak sesuai sehingga tidak terbaca dengan jelas dan informasi tidak di dapat oleh pengguna. Penelitian ini menggunakan metode UCD ( User Centered Design), yang mana metode UCD dapat menempatkan pengguna sebagai pusat pengembangan desain . Metode ini dapat digunakan untuk mengetahui seperti apa karakter dan kebutuhan dari pengguna. Untuk mengetahaui tingkat usability design yang sudah dibuat, dilakukan pengujian dengan penyebaran kueisioner dalam bentuk google form dengan hasil pengujian yang pertama sebesar 79,81 masuk ke dalam kriteria (B) Baik. Kemudian dilakukan pengujian kedua mendapatkan hasil 85,27 dengan iterasi sebanyak dua kali setelah melalui proses UCD. Berdasarkan hasil tersebut, desain website berita mobile bagi penyandang rabun dekat termasuk ke dalam kriteria (SB) Sangat Baik
Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android Pangestu, Yoga; Sanjaya, Suwanto; Jasril; Agustian, Surya; Safaat, Nazruddin
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 2 (June 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i2.1195

Abstract

The advancement of digital technology has generated a demand for applications that assist the public in ensuring the halal status of food products, particularly in distinguishing between beef and pork. This study aims to develop an Android-based application for detecting beef and pork using Deep Learning methods with the EfficientNet-B6 architecture, employing the eXtreme Programming software development approach. The image classification model utilizes a Convolutional Neural Network architecture integrated into a Python-based server, while the user interface is developed with Java in Android Studio. System testing was conducted using black-box methods on several Android devices, with varying room conditions and meat types. The results show that the application can classify meat with an accuracy of 66.7%, considering room conditions such as light and dark environments, and meat types including fatty and non-fatty. This application provides fast response times and a user-friendly interface. This application is expected to enable users to independently and efficiently verify the halal status of meat, thereby supporting the needs of Muslim consumers in the digital era.
Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android Pangestu, Yoga; Sanjaya, Suwanto; Jasril; Agustian, Surya; Safaat, Nazruddin
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 2 (June 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i2.1195

Abstract

The advancement of digital technology has generated a demand for applications that assist the public in ensuring the halal status of food products, particularly in distinguishing between beef and pork. This study aims to develop an Android-based application for detecting beef and pork using Deep Learning methods with the EfficientNet-B6 architecture, employing the eXtreme Programming software development approach. The image classification model utilizes a Convolutional Neural Network architecture integrated into a Python-based server, while the user interface is developed with Java in Android Studio. System testing was conducted using black-box methods on several Android devices, with varying room conditions and meat types. The results show that the application can classify meat with an accuracy of 66.7%, considering room conditions such as light and dark environments, and meat types including fatty and non-fatty. This application provides fast response times and a user-friendly interface. This application is expected to enable users to independently and efficiently verify the halal status of meat, thereby supporting the needs of Muslim consumers in the digital era.
Perbandingan Performa Metode Klasifikasi Teks Multilabel Hadis Terjemahan Bukhari Menggunakan Support Vector Machine dan Long Short Term Memory: Performance Comparison of Multilabel Text Classification Methods on Translated Hadiths of Bukhari Using Support Vector Machine and Long Short Term Memory Ramadhani, Aulia; Safaat, Nazruddin; Agustian, Surya; Iskandar, Iwan; Sanjaya, Suwanto
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2051

Abstract

Hadis merupakan sumber hukum kedua dalam Islam, dan salah satu kitab hadis yang paling dikenal adalah Shahih al-Bukhari. Untuk mendukung pemahaman dan pengamalan yang tepat, hadis perlu diklasifikasikan secara akurat. Mengingat satu hadis dapat mengandung lebih dari satu informasi, pendekatan klasifikasi multilabel menjadi sangat relevan. Penelitian ini bertujuan untuk memberikan kontribusi dalam bidang klasifikasi teks dengan mengeksplorasi kombinasi metode dan parameter yang optimal untuk klasifikasi multilabel hadis. Hasil penelitian menunjukkan bahwa Support Vector Machine (SVM) memberikan performa terbaik pada label Larangan dengan Macro F1-score sebesar 82,57%, melalui kombinasi SVM + TF-IDF menggunakan kernel = linear, parameter C (regularization parameter) = 1 tanpa stopword removal dan tanpa balancing. Sementara itu, Long Short Term Memory (LSTM) juga unggul pada label Larangan dengan Macro F1-score 82,66% pada kombinasi parameter Epoch = 20, Dropout = 0.5, Dense = 128 dan Batch Size = 64 tanpa stopword removal dan tanpa balancing kombinasi ini juga menghasilkan nilai Hamming Loss terendah sebesar 10,452%, yang lebih baik dibandingkan dengan penelitian sebelumnya serta menunjukkan bahwa LSTM terbukti lebih efektif secara keseluruhan dengan penyetelan parameter yang tepat. Penelitian ini juga berkontribusi dalam peningkatan kualitas data dengan melengkapi matan hadis yang digunakan, sehingga menghasilkan performa klasifikasi yang lebih baik.
PENINGKATAN KINERJA SUPPORT VECTOR MACHINE MENGGUNAKAN MODEL BAHASA BERT UNTUK KLASIFIKASI SENTIMEN DENGAN DATASET TERBATAS Iffa, Marwika Rifattul; Agustian, Surya; Safaat, Nazruddin; Irsyad, Muhammad
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 2 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Mei 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i2.26847

Abstract

Media sosial kini menjadi ruang penting bagi masyarakat untuk mengekspresikan opini secara terbuka terhadap berbagai isu terkini, salah satunya melalui platform X  yang populer di kalangan pengguna internet. Platform ini sering digunakan sebagai sumber data klasifikasi sentimen guna mengungkap persepsi masyarakat terhadap peristiwa-peristiwa yang terjadi, khususnya di bidang politik dan pemerintahan. Namun, keterbatasan dataset menjadi tantangan utama dalam proses klasifikasi karena kondisi tersebut dapat mempengaruhi akurasi dan validitas sentimen yang dihasilkan. Untuk mengatasi permasalahan tersebut, penelitian ini mengusulkan kombinasi algoritma Support Vector Machine (SVM) dengan fitur Bidirectional Encoder Representations from Transformers (BERT) yang terbukti efektif dalam menangkap konteks bahasa secara mendalam. Pendekatan ini bertujuan untuk meningkatkan performa klasifikasi sentimen terkait pengangkatan Kaesang Pangarep sebagai Ketua Umum Partai Solidaritas Indonesia (PSI) pada media sosial X. Metode penelitian meliputi tahap preprocessing text, ekstraksi fitur menggunakan BERT, serta penerapan SVM dalam proses klasifikasi sentimen. Hasil eksperimen menunjukkan bahwa model kombinasi tersebut berhasil meningkatkan F1-Score secara signifikan sebesar 3% pada data uji. Hal ini menandakan model bahasa BERT dapat meningkatkan performa SVM dalam klasifikasi sentimen
Analisis dan Desain Data Center RSUD Arifin Achmad Pekanbaru Menggunakan Standarisasi TIA 942 Syaputra, Alviandy; Iskandar, Iwan; Darmizal, Teddie; Novriyanto, Novriyanto; Safaat, Nazruddin
Jurnal Informatika Universitas Pamulang Vol 8 No 4 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i4.36564

Abstract

Arifin Achmad Regional General Hospital (RSUD) has a large amount of patient data so it requires a data center to store and manage all the data. In this study, an analysis of the data center at RSUD Arifin Achmad was carried out using the TIA-942 standard. Based on the results of observations that have been made, it is obtained that the current condition of the data center has several shortcomings, including the electrical system that does not yet have a private generator as a redudant, the security system that is still minimal, and the room conditions that are still limited. Based on these problems, an analysis was carried out using the PPDIOO (Prepare, Plan, Design, Implement, Operate, and Optimize) Network Life Cycle Approach method with the TIA-942 standardization approach In this research, it has been carried out up to the design stage, where at the prepare stage, search and collect related information, interview experts to gain a better understanding of the TIA-942 standard, at the planning stage (plan) a comparative analysis of the current data center with the TIA-942 standard using GAP analysis, and at the design stage (design) the design of the proposed Tier 2 data center is made. The results of this study are the current condition of the data center still in Tier 1 and provide recommendations for proposals in the form of data center designs at Tier 2 in accordance with the TIA-942 standard.
Perbandingan Performa Klasifikasi Terjemahan Al-Qur'an Menggunakan Metode Random Forest dan Long Short Term Memory Aftari, Dhea Putri; Safaat, Nazruddin; Agustian, Surya; Yusra, Yusra; Afrianty, Iis
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5156

Abstract

This study focuses on the use of the Qur'an as the primary source of Islamic teachings, aiming to facilitate Muslims' understanding of its content. To achieve this, the classification of translated Qur'anic verses was conducted. Two methods that are rarely used for Qur'anic translation data are Random Forest (RF) and Long Short Term Memory (LSTM) due to their ability to process large and complex data. The data used in this study are translations of the Qur'an that have been classified into 15 topics by previous research, but this study will only focus on 6 topics. The objective of this research is to compare the performance of RF and LSTM in classifying Qur'anic translations into 6 different categories. The results show that in the preaching category, LSTM consistently outperformed RF, with an F1-Score of 57.3% and an accuracy of 96.8%, whereas RF achieved an F1-Score of 49.4% and an accuracy of 97.5%. These findings indicate that LSTM has better performance, especially with proper preprocessing, optimal parameter tuning, and balanced data. This study provides important insights into the development of classification models for Qur'anic translation texts, highlighting the importance of proper preprocessing and parameter tuning.
Pengaruh Penyeimbangan Data Pada Klasifikasi Terjemahan Al-Quran Dengan Metode Naïve Bayes dan Long Short Term Memory Ningsih, Sulistia; Safaat, Nazruddin; Agustian, Surya; Yusra, Yusra; Cynthia, Eka Pandu
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5181

Abstract

The Al Qur'an is a holy book of Muslims which is a guide to life for all mankind. Studying and understanding the translation of the Al-Quran is not easy, one way that can be done is to classify the translation of Al-Quran verses into existing topics. This research uses Naïve Bayes and LSTM methods in the classification process. The data used comes from translation data of the Al-Quran in Indonesian which has been labeled based on multi-class classification. One of the main problems faced is data imbalance. To overcome this problem, data balancing, text preprocessing, feature construction and feature extraction processes were carried out using the Bag of Words (BoW) and TF.IDF techniques. The research results indicate that the most optimal Naïve Bayes model achieved an average accuracy of 55.39% on test data from juz 30, 61.59% on test data from juz 10-20, and 59.53% on test data from juz 25-28. Meanwhile, the most optimal LSTM model yielded an accuracy of 58.02% on test data from juz 30, 59.64% on test data from juz 10-20, and 58.59% on test data from juz 25-28. The main aim of this research is to improve classification performance and compare the accuracy between naïve Bayes and lstm.
Penerapan Desain UI/UX Pada Aplikasi Buku Kas Laundry Menggunakan Metode Lean Ux Nabawi, M Reza; Wulandari, Fitri; Safaat, Nazruddin; Darmizal, Teddie
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5082

Abstract

Laundry services are businesses in the field of washing and drying services, especially clothes. This business is growing as the number of consumers who use laundry services increases, because it can help make things easier for busy people in their daily work. Using laundry services can be used as an alternative way to help with their clothes washing activities. With digital developments, laundry owners have started using cash book applications to make their work easier, such as recording orders, recording expenses and recording income. However, the cash book recording carried out in the cash book application is currently done manually, Previous users have used existing applications, because users feel uncomfortable with the appearance, such as colors that are uncomfortable to look at, fonts that are too small and menu buttons that are confusing. So that it encourages researchers to make a redesign of the application design. Based on these problems, a redesign of the laundry cash book application is needed which can provide comfort for users who will use the application. The method used to solve these problems uses the Lean Ux method. Using the Lean Ux method will make it easier for the author to get feedback faster. Designing a prototype laundry cash book application with an emphasis on User Interface (UI) and User Experience (UX) is the aim of this research. This research uses four stages of the Lean UX methodology, namely Declare Assumptions, Create a Minimum Viable Product, Run an Experiment and Feedback and Research to develop problem assumptions, prototype design, internal experiments and usability testing. Using the System Usability Scale approach, a prototype was created using the Adobe XD program with 20 respondents who received a score of 87 with adjective ratings excellent.
Co-Authors Aftari, Dhea Putri Agustian, Surya Aulia Ramadhani Darmizal, Teddie Eka Pandu Cynthia, Eka Pandu Fitri Wulandari Iffa, Marwika Rifattul Iis Afrianty Iwan Iskandar Jasril Jasril Muhammad Affandes Muhammad Irsyad Nabawi, M Reza Novriyanto Novriyanto Pangestu, Yoga Reski Mai Candra Sulistia Ningsih, Sulistia Suwanto Sanjaya Syaputra, Alviandy Tania, Windy Yusra, Yusra