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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Mantik Tematik : Jurnal Teknologi Informasi Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Kajian Ilmiah Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat IICS Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Abdimas Ekonomi Dan Bisnis (JAMEB) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan SIGMA: Information Technology Journal Jurnal Sistim Informasi dan Teknologi Jurnal Pengabdian Pelitabangsa International Journal of Information Technology and Computer Science Applications (IJITCSA) Jurnal Sistem Informasi Indonesian Journal of Education And Computer Science Jurnal Inovasi dan Pengembangan Hasil Pengabdian Masyarakat VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Journal of Computer Science Contributions (Jucosco) The Indonesian Journal of Computer Science Journal of Informatics and Information Security
Priatna , Wowon
Informatika, Fakultas Ilmu Komputer, Universitas Bhayangkara Jakarta Raya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Public Health Social Sciences Other

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Implementasi Deep Learning Untuk Rekomendasi Aplikasi E-learning Yang Tepat Untuk Pembelajaran jarak jauh Wowon Priatna; Rakhmat Purnomo; Tri Dharma Putra
Jurnal Kajian Ilmiah Vol. 21 No. 3 (2021): September 2021
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (554.294 KB) | DOI: 10.31599/jki.v21i3.521

Abstract

The purpose of this study is to recommend e-learning applications that are appropriate for use in online learning in college environments. The large number of e-learning platforms used by lecturers for online lecture activities results in students being forced to use several e-learning applications depending on the lecturer who teaches the courses taken, for the university also finally gives lecturers policies for distance learning reports each finished giving the material. In this study the data collection method began by taking data from the faculty to find out which e-learning applications were widely used by lecturers, then distributing questionnaires to students and lecturers who used the e-learning application to measure the e-leaning application with the e-learning criteria. Appropriate. The data is then processed into a dataset. The algorithm used in implementing deep learning is Artificial Neural Network (ANN). For the implementation of ANN, 27 variables were determined from the e-learning criteria and 1 target. In this ANN stage, prediction was used with classifications based on preparation, training, learning, evaluation and prediction using the python programming. The results obtained in this study that the Moodle application gets the highest score with an accuracy of 97% to be used as a recommendation for e-learning applications that are appropriate for universities to conduct online lectures.
Desain dan Implementasi Sistem Monitoring Daya Pintar untuk CCTV Berbasis IoT dengan Model Scrum Mahbub, Asep Ramdhani; Warta, Joni; Hidayat, Agus; ., Rasim; Priatna, Wowon
JSI (Jurnal sistem Informasi) Universitas Suryadarma Vol 12, No 1 (2025): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Universitas Dirgantara Marsekal Suryadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v12i1.1325

Abstract

Penelitian ini mengembangkan aplikasi monitoring daya pintar CCTV berbasis IoT menggunakan metode Scrum untuk meningkatkan efisiensi energi dan keamanan sistem pengawasan. Tantangan utama yang dihadapi adalah pengelolaan energi yang efisien dalam jaringan CCTV yang luas. Dengan mengadopsi teknologi Internet of Things (IoT), sistem ini memungkinkan pemantauan dan analisis data konsumsi daya secara real-time, sekaligus memperlancar otomatisasi pengelolaan daya. Pengembangan aplikasi dilakukan menggunakan arsitektur tiga tingkat dan menerapkan proses iteratif Scrum yang adaptif terhadap perubahan kebutuhan sistem. Hasil penelitian menunjukkan peningkatan yang signifikan dalam efisiensi energi serta keamanan operasional CCTV, sambil memastikan skalabilitas dan fleksibilitas sistem. Studi ini memberikan kontribusi penting dalam pengembangan aplikasi IoT yang efisien di sektor keamanan, menawarkan pendekatan yang praktis untuk monitoring daya pintar dalam jaringan CCTV.
Particle Swarm Optimization for Optimizing Public Service Satisfaction Level Classification Lestari, Tyastuti Sri; Ismaniah, Ismaniah; Priatna, Wowon
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.69612

Abstract

This research aims to categorize survey data to determine the level of satisfaction with the services provided by the village government as a public service provider. Villages or sub-districts currently offer services in response to community demand, although only partially or as efficiently as possible. The data collection technique used was distributing questionnaires to the village community. The method used for classification is the machine learning method. Before the classification process, feature selection is carried out at the data pre-processing stage using Particle Swarm Optimization (PSO), which has been proven to increase the accuracy of the classification values. The classification methods employed include Decision Tree (DT), Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms for classification purposes. This study achieves the maximum level of accuracy in decision tree classification, attaining an accuracy rate of 97.74%. Subsequently, the KNN algorithm achieved an accuracy of 77.90%, the Nave Bayes algorithm achieved 64.4%, and the SVM algorithm, which yielded the lowest accuracy value, achieved 59.90%. Following the application of Particle Swarm Optimization (PSO) for optimization, the accuracy of the SVM and KNN algorithms improved to 98.3%. The Decision Tree algorithm achieved a value of 97.77%, while the Naive Bayes technique yielded a value of 69.30%.
Network Intrusion Detection Using Transformer Models and Natural Language Processing for Enhanced Web Application Attack Detection Priatna, Wowon; Sembiring, Irwan; Setiawan, Adi; Setyawan, Iwan
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.82462

Abstract

The increasing frequency and complexity of web application attacks necessitate more advanced detection methods. This research explores integrating Transformer models and Natural Language Processing (NLP) techniques to enhance network intrusion detection systems (NIDS). Traditional NIDS often rely on predefined signatures and rules, limiting their effectiveness against new attacks. By leveraging the Transformer's ability to capture long-term dependencies and the contextual richness of NLP, this study aims to develop a more adaptive and intelligent intrusion detection framework. Utilizing the CSIC 2010 dataset, comprehensive preprocessing steps such as tokenization, stemming, lemmatization, and normalization were applied. Techniques like Word2Vec, BERT, and TF-IDF were used for text representation, followed by the application of the Transformer architecture. Performance evaluation using accuracy, precision, recall, F1 score, and AUC demonstrated the superiority of the Transformer-NLP model over traditional machine learning methods. Statistical validation through Friedman and T-tests confirmed the model's robustness and practical significance. Despite promising results, limitations include the dataset's scope, computational complexity, and the need for further research to generalize the model to other types of network attacks. This study indicates significant improvements in detecting complex web application attacks, reducing false positives, and enhancing overall security, making it a viable solution for addressing increasingly sophisticated cybersecurity threats
Implementasi Fuzzy Logic Pada Sistem Kontrol pH Air Mineral Berbasis IOT Joniwarta; Priatna, Wowon; Hamdani, Asep R.; Alexander, Allan D.
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3356

Abstract

Implementation of Fuzzy Logic in the IOT-Based Mineral Water pH Control System for various existing bottled mineral water products, this system can measure the pH value, to find out if the value is still within the limits suitable for consumption or not based on government regulations. The public finds it challenging to understand the level of the pH value of the product because the current state of information regarding the pH level of mineral water generally is not listed in the mineral water bottle circulating on the market. Hardware design, application design, and hardware and software integration were the three steps of this research project. The pH value will be read by this control system from the output of the sensors then the data is collected into a data set. The data will be examined for trends using fuzzy logic, which will be used to classify the maximum and minimum pH levels, acidity levels, and base levels. The study's findings show that an internet-based web of things can access the mineral water pH control system to ascertain each mineral water product's pH value and temperature. This information can then be used by consumers to ascertain the pH level of each mineral water product.
Particle Swarm Optimization Untuk Optimasi Klasifikasi Tingkat Kepuasan Layanan Publik Priatna, wowon
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3441

Abstract

Tujuan dari penelitian ini adalah untuk mengetahui tingkat kepuasan terhadap pelayanan yang diberikan oleh pemerintah daerah sebagai penyedia layanan publik dengan mengklasifikasikan data yang diperoleh dari survei yang dilakukan. Saat ini desa dan kelurahan telah memberikan pelayanan sesuai kebutuhan masyarakat, namun jika tidak sepenuhnya memberikan pelayanan yang optimal maka dapat menimbulkan ketidakpuasan dan merugikan masyarakat baik secara fisik maupun materil. Untuk meningkatkan kualitas layanan dan menyelesaikan keluhan pengguna layanan secara efektif, mengidentifikasi pola dan memberikan umpan balik yang tepat waktu untuk meningkatkan produk dan layanan yang diberikan, diperlukan metode klasifikasi pengguna layanan. Metode pengumpulan data pada penelitian ini menggunakan metode survei dengan menyebarkan kuesioner kepada masyarakat pengguna layanan publik di desa dan kelurahan. Data yang diperoleh dianalisis menggunakan Excel untuk mengolah data terlebih dahulu untuk membuat model klasifikasi. Pada tahap prepROCessing, data dikelompokkan untuk mendapatkan label/target sehingga data tersebut dapat diolah menggunakan algoritma klasifikasi. Klasifikasinya menggunakan algoritma Decision Tree (DT), Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN). Tingkatkan klasifikasi dengan pengoptimalan fitur menggunakan Particle Pool Optimization (SPO). Penelitian ini menghasilkan nilai akurasi tertinggi pada klasifikasi pohon keputusan dengan mendapatkan nilai akurasi tertinggi sebesar 97,74%, disusul algoritma KKN memperoleh akurasi sebesar 77,90%, algoritma Naïve Bayes sebesar 64,4% dan algoritma yang memperoleh nilai akurasi terkecil adalah algoritma SVM. yaitu 59,90%. Setelah dilakukan optimasi, nilai akurasi tertinggi terdapat pada algoritma SVM dan algoritma KNN sebesar 98,3%, pohon keputusan sebesar 97,77%, dan akurasi terkecil pada algoritma Naïve Bayes sebesar 69,30%.
Dampak Pengambilan Sampel Data untuk Optimalisasi Data tidak seimbang pada Klasifikasi Penipuan Transaksi E-Commerce Priatna, Wowon
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3698

Abstract

Tujuan dari penelitian ini adalah untuk mengatasi masalah pengklasifikasian dan prediksi data yang tidak seimbang terkait dengan kondisi transaksi E-Commerce. Menjamurnya transaksi e-commerce menimbulkan potensi permasalahan: penipuan dalam pembelian e-commerce. Kasus penipuan e-niaga terus meningkat setiap tahun sejak tahun 1993. Menurut survei tahun 2013, untuk setiap $100 transaksi e-niaga, terdapat kerugian sebesar 5,65 sen akibat penipuan. Mendeteksi penipuan merupakan pendekatan yang efektif untuk meminimalkan terjadinya aktivitas penipuan dalam transaksi e-commerce. Pembelajaran menjadi metode yang semakin dapat diandalkan untuk memprediksi keadaan. Tidak adanya keseimbangan antara data yang curang dan tidak curang mengakibatkan klasifikasi menjadi bias. Algoritma SMOTE diperlukan untuk mencapai keseimbangan data. Selanjutnya peristiwa transaksi akan diklasifikasikan menggunakan algoritma Support Vector Machine, K-Nearest Neighbor, Naive Bayes, dan C45, dengan mempertimbangkan hasil penyeimbangan data. Di antara algoritma SVM, KNN, dan C45, metode Naive Bayes menunjukkan nilai akurasi tertinggi. Oleh karena itu, disarankan untuk menggunakan teknik ini untuk tujuan mengidentifikasi kondisi e-commerce
Crawling Engine Pada Website Mann, Baldwin, Fleetguard Dan Pengelompokan Produk Menggunakan K-Means Rahman, Andi; Priatna, Wowon; Lestari, Tyastuti Sri; Hidayat, Agus
Jurnal Pelita Teknologi Vol 19 No 2 (2024): September 2024
Publisher : Universitas Pelita Bangsa

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

Abstract

Tujuan penelitian ini adalah untuk mengelompokan produk pada beberapa web site. Dalam crawling engine akan sangat membantu dalam memasukan data produk secara otomatis mengambil data dari website produk tersebut, kemudian di input dalam aplikasi Odoo. Algoritma k-means klustering sendiri adalah algoritma mengelompokkan pengamatan ke dalam kelompok k, di mana k merupakan parameter input. Tiap data kemudian ditetapkan pada setiap pengamatan cluster berdasarkan kedekatan pengamatan nilai rata-rata cluster. Pengelompokan ini akan sangat membantu dalam klasifikasi produk berdasarkan cross reference. Hasil dari penelitian ini adalah produk produk terinput secara otomatis dan data sesuai dengan website produk tersebut dan produk terkelompok sesuai dengan cross reference.
Implementasi Algoritma Naïve Bayes dan Algoritma C4.5 Untuk Melakukan Analisis Sentimen terhadap Ulasan Komentar Pengguna TikTok di Google Play Store Aprilyana, Dhea Putri; Priatna, Wowon; Setiawati, Siti
Jurnal Pelita Teknologi Vol 19 No 1 (2024): Maret 2024
Publisher : Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/pelitatekno.v19i1.2488

Abstract

TikTok is a popular application among young people. TikTok was an application initially launched in China before landing in Indonesia at the end of 2017. Unfortunately, the popularity of TikTok stems from personal lack of self-image, for example wearing sexy clothes, dancing in erotic and inappropriate moves. This is based on many positive and negative comments from TikTok users. So we need a way to automatically classify reviews through sentiment analysis. The purpose of this study is to classify TikTok user comments on Google Play Store using Naive Bayes and C4.5 algorithms. This study used 1330 data, of which 602 data were negative and 728 data were positive. The results show that the Naive Bayes algorithm produces accuracy values ​​of 79.00%, 79.00% precision, 78.00% recall, and 78.00% F1 score. The C4.5 algorithm produces 68.00% accuracy, 68.00% precision, 68.00% recall, and 68.00% F1 score. We can conclude that the Naive Bayes algorithm is the best algorithm compared to the C4.5 algorithm. The Naive Bayes algorithm achieves an accuracy value of 79.00%.
Algoritma First in First Out (FIFO) Untuk Perancangan Aplikasi Pemesanan Kaos Sablon Widianto, Ilham Rizky; Priatna, Wowon; Lubis, Hendarman
Jurnal Kajian Ilmiah Vol. 23 No. 2 (2023): May 2023
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/tva3pd96

Abstract

The purpose of this study is to solve the problem of screen-printing T-shirt shops. For manual screen printing t-shirt shops, customers often have to visit the store in person or contact them via chat or phone, often encountering the following issues when ordering t-shirts: B. Irregular orders for those who have placed an order in advance or who have been waiting for a long time. One way to solve the queuing problem is the FIFO algorithm. FIFO algorithms are methods for organizing, processing, and manipulating basic data structures in computer systems. The FIFO algorithm phases in this study begin with the data preparation phase, the Gantt cart process, and finally his FIFO wait time. The result of the FIFO stage translates into creating applications using the Java programming language, Android Studio, and the FireBase database. The results of this study can be applied to his FIFO algorithm for customer queues in ordering T-shirts. A t-shirt ordering application was tested using the white box method by running the test case in four passes. All tests passed, so you can use the ordering application based on the FIFO algorithm.
Co-Authors -, Rasim ., Rasim Ade Iriani Adi Setiawan Agung Nugroho Agung Nugroho Agus Hidayat Agus Hidayat Aida Fitriyani, Aida Ajif Yunizar Pratama Yusuf Alexander, Allan D Alexander, Allan D. Alhillah, Yumaris Alfi Andi Lawrence Hutahaean, Johanes Andi Rahman Andri Fajriya Andry Fadjriya Annisa Oktavianti Hermadi Aprilyana, Dhea Putri Asep R. Hamdani Asep Ramdhani M Asep Ramdhani Mahbub Atika , Prima Dina Dimas Abimanyu Prasetyo Dwi Budi Srisulistiowati Dwipa Handayani Eka Nur A’ini Endang Retnoningsih Enggar Putera, dkk, Diaz Faisal Adi Saputra Fajar Mukharom Fathurrazi, Ahmad Febry Sandrian Sagala Fefbiansyah Hasibuan Galih Apriansha Pradana Hadi Kusmara Hamdani, Asep R. Hendarman Lubis Herlawati Herlawati Hindriyanto Dwi Purnomo Ikhsan Romli Ilham Rizky Widianto Irwan Sembiring Ismaniah, Ismaniah Iwan Setyawan Joni Warta Joni Warta Joniwarta Joniwarta Jumi Saroh Hidayat Kapriadi, Engkap Karyaningsih, Dentik Khoirunnisaa, Nabiilah Kustanto , Prio Lestari, Tyastuti Sri Lubis, Hendarman M. Fadhli Nursal Mahbub, Asep Ramdhani Mayadi Mayadi Meutia, Kardinah Indrianna Mugiarso Mugiarso, Mugiarso Muhammad Khaerudin Noe’man,, Achmad Nurjeli Nurjeli Pradana , Galih Apriansha Prima Dina Atika Purnomo, Rakhmat Rahmadya Trias Handayanto Rakhmat Purnomo Rasim Rejeki , Sri Retnoningsih , Endang Rinaldi Tunnisia Ritzkal, Ritzkal Sagala, Febry Sandrian Saputra , Faisal Adi Silvi - Siti Setiawati SITI SETIAWATI Siti Setiawati Siti Setiawati, Andika Yusuf Hidayat Sri Lestari, Tyastuti Sri Rejeki Sudiantini, Dian Sulistiyo, Dwi Suryadi Syahbaniar Rofiah Tb Ai Munandar, Tb Ai Theopillus J. H. Wellem Tri Dharma Putra Tri Dharma Putra Tyastuti Sri Lestari Tyastuti Sri Lestari Tyastuti Sri Lestari Tyastuti Sri Lestari Widianto, Ilham Rizky Wiyanto Wiyanto