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All Journal International Journal of Electrical and Computer Engineering Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Voteteknika (Vocational Teknik Elektronika dan Informatika) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence JTET (Jurnal Teknik Elektro Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal SOLMA Jurnal Telematika Jurnal Teknologi Terapan Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Infotekmesin JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Komputer dan Informatika (JSON) Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Madaniya Jurnal Teknologi Informasi dan Komunikasi Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Pengabdian Teknologi Tepat Guna Jurnal Pengabdian Kepada Masyarakat (JPKM) Langit Biru Jurnal Nasional Teknik Elektro dan Teknologi Informasi JAMAIKA: Jurnal Abdi Masyarakat JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Journal of Applied Community Engagement (JACE) Pengabdian Jurnal Abdimas Hikmayo: Jurnal Pengabdian Masyarakat SmartComp Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT
Oman Somantri
Politeknik Negeri Cilacap
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Metode K-Means untuk Optimasi Klasifikasi Tema Tugas Akhir Mahasiswa Menggunakan Support Vector Machine (SVM) Somantri, Oman; Wiyono, Slamet; Dairoh, Dairoh
Scientific Journal of Informatics Vol 3, No 1 (2016): May 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i1.5845

Abstract

Masih sulitnya dalam menentukan klasifikasi tema tugas akhir mahasiswa sering dialami oleh setiap perguruan tinggi. Algoritma SVM digunakan untuk mengklasifikasi jenis tema tugas akhir mahasiswa. SVM merupakan metode yang banyak digunakan untuk klasifikasi. K-Means Clustering merupakan metode pengelompokan paling sederhana yang mengelompokkan data kedalam k kelompok berdasar pada centroid masing-masing kelompok. Optimasi klasifikasi tema tugas akhir mahasiswa menggunakan SVM dan K-Means untuk meningkatkan tingkat akurasi. Hasil yang diperoleh memiliki tingkat akurasi yang lebih baik yaitu 86,21%. 
Implementasi Sistem Informasi Arsip Desa Widarapayung Wetan Cilacap Untuk Peningkatan Pelayanan Pemerintah Ratih Hafsarah Maharrani; Prih Diantono Abda’u; Muhammad Nur Faiz; Agus Susanto; Hety Dwi Astuti; Oman Somantri; Santi Purwaningrum
Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 5, No 1 (2022): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v5i1.3099

Abstract

Kondisi pengelolaan arsip di Desa Widarapayung Wetan Cilacap saat ini masih dilakukan secara konvensional, sedangkan kecepatan dan ketepatan dalam pencarian data arsip surat sangat berpengaruh kepada kualitas layanan. Dokumentasi surat masuk dan surat keluar saat ini masih secara konvensional dan berbentuk catatan yang ditulis pada buku besar berakibat sering terjadinya kesalahan dalam peyimpanan data kearsipan dan pencarian surat. Berdasarkan kesepakatan, untuk mengatasi permasalahan maka diberikanlah solusi dengan membangun sebuah aplikasi sistem informasi arsip yang dimplementasikan di desa Widarapayung Wetan Cilacap dengan harapan masalah dapat teratasi dan adanya peningkatan pelayanan. Tahapan kegiatan yang dilakukan yakni dengan melakukan assessment permasalahan, perencanaan kegiatan, pembangunan sistem, ujicoba sistem, implementasi dan evaluasi sistem, serta tahapan akhir adalah pelatihan user dan pendampingan. Berdasarkan hasil pembangunan sistem yang telah dibuat Desa Widarapayung Wetan Cilacap saat ini sudah dapat berupaya untuk meningkatkan pelayanan khususnya dalam pengelolaan arsip yang sudah dapat diakukan secara digital.
SentiHotel: a sentiment analysis application of hotel services using an optimized neural network Dyah Apriliani; Taufiq Abidin; Edhy Sutanta; Amir Hamzah; Oman Somantri
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3040

Abstract

An assessed hotel service is necessary for tourists and everyone who is traveling, however currently it is still difficult to find recommended hotel information. The solution provided in this research is to propose a smart application that has been developed by implementing machine learning in it. The purpose is to build a sentiment review smart application by applying the sentiment analysis hybrid model of the best neural network (NN) algorithm model that has been optimized using genetic algorithms. To get the right model, the research method was carried out with experiments starting from the initial stages of conducting data preprocessing, tokenization, weighting, modeling experiments, and conducting the system evaluation stage to determine the success of the proposed model. The progress of the application development system is by using the prototyping model. SentiHotel is a sentiment application that was successfully built to provide a solution for tourists in assessing a hotel service. The software validation test is carried out using the blackbox method and the results show that the SentHotel application is in accordance with the expected result; all system functions can run properly.
Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review Muhammad Nur Faiz; Oman Somantri; Abdul Rohman Supriyono; Arif Wirawan Muhammad
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6112

Abstract

Cybersecurity attacks are becoming increasingly sophisticated and increasing with the development of technology so that they present threats to both the private and public sectors, especially Denial of Service (DoS) attacks and their variants which are often known as Distributed Denial of Service (DDoS). One way to minimize this attack is by using traditional mitigation solutions such as human-assisted network traffic analysis techniques but experiencing some limitations and performance problems. To overcome these limitations, Machine Learning (ML) has become one of the main techniques to enrich, complement and enhance the traditional security experience. The way ML works are based on the process of data collection, training and output. ML is influenced by several factors, one of which is feature engineering. In this study, we focus on the literature review of several recent studies which show that the feature selection process greatly impacts the level of accuracy of this ML. Datasets such as KDD, UNSW-NB15 and others also affect the level of accuracy of ML. Based on this literature review, this study can observe several feature engineering strategies with relevant impacts that can be chosen to improve ML solutions on DDoS attacks.
An Optimize Weights Naïve Bayes Model for Early Detection of Diabetes Oman Somantri; Ratih Hafsarah Maharrani; Linda Perdana Wanti
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1307

Abstract

This research proposes a method to optimize the accuracy of the Naïve Bayes (NB) model by optimizing weight using a genetic algorithm (GA). The process of giving optimal weight is carried out when the data will be input into the analysis process using NB. The research stages were conducted by preprocessing the data, searching for the classic naïve Bayes model, optimizing the weight, applying the hybrid model, and as the final stage, evaluating the model. The results showed an increase in the accuracy of the proposed model, where the naïve Bayes classical model produced accuracy rate of 87.69% and increased to 88.65% after optimization using GA. The results of the study conclude that the proposed optimization model can increase the accuracy of the classification of early detection of diabetes.
Metode Naive Bayes Dalam Menentukan Program Studi Bagi Calon Mahasiswa Baru Wildani Eko Nugroho; Ali Sofyan; Oman Somantri
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.491

Abstract

In a university, determining a study program for prospective students is something that is often done to focus on prospective students so that they are in accordance with their competencies. This is a very important hope, because prospective students can develop self-competence according to their academic abilities. This research method uses several stages, including data cleaning, data collection, determining criteria, determining probability, and final testing. The Naïve Bayes method with a case study at the Private Madrasah Aliyah PAB 6 Helvetia and testing of 100 student data with an accuracy rate of 90% is a previous research. The purpose of this study was to make a classification of majors based on the criteria, while in this study the aim of making a classification of study programs for prospective new students. In this study, the same method was used but the number of data records was different, the test data was 1671 student data records, the data was obtained from 2256 data records.From the total data records were 2256, after data cleaning and data collection were carried out, 1671 test data were obtained. In the test data, there are several probability values that contain various criteria and attributes used to determine the classification of study programs for prospective new students. The number of data records is divided into 2 parts, the first is used for training data with 1158 data with a percentage of 70%, and testing data with 513 data records with a percentage of 30%. From the test results with the same method with different number of data records, the accuracy rate is from 90% to 96% with an accuracy value of 96.68%. From this accuracy value shows that the classification results obtained show the Pharmacy DIII study program.
Optimalisasi Metode Naive Bayes untuk Menentukan Program Studi bagi Calon Mahasiswa Baru dengan Pendekatan Unsupervised Discretization Wildani Eko Nugroho; Teguh Prihandoyo; Oman Somantri
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.1048

Abstract

The admission of prospective new students must consider various procedures to direct prospective new students in determining the study program they are interested in. This study will discuss the optimization of the Naive Bayes method to determine the study program or major for prospective new students with the Unsupervised Discritization method approach. There are several stages of research methods carried out in this study, including Data Cleaning, Data Collection, Criteria Determination, Probability Determination, and Data Testing. This research has been carried out using the same method, namely the Naïve Bayes method which is used to classify the interests of prospective new students in determining the study program with an accuracy value of 96.68%. Ongoing research uses the same method, namely Naive Bayes, then optimization is carried out with the Unsupervised Discretization method approach. For data testing, there are 1671 student data records. After testing with the same method and optimizing it, the accuracy value from 96.68% became 97.66% with the classification results showing the DIII Pharmacy study program. The purpose of this research is to produce a classification in determining the study program or major for prospective new students using the Naïve Bayes method by the optimization of the Unsupervised Discretization method. From the results of testing the data, the Naïve Bayes method after optimization with the Unsupervised Discretization method is very good compared to the application before optimization.
Grouping Seleksi Penempatan Kelas Industri Untuk Siswa Menggunakan Multi-Objective Optimization on basis of Ratio Analysis Ratih Hafsarah Maharrani; Oman Somantri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 2 (2020): April 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (633.544 KB) | DOI: 10.29207/resti.v4i2.1532

Abstract

The quality and success of a company is determined from the planning and efforts to meet the needs of Human Resources carried out in the selection process. This means that in an effective selection process good human resources can be obtained for a longer period of time. In this study, the effectiveness of student values ​​will be analyzed with a decision support system that is used to facilitate management in the selection stage of industrial class students. From the results of the assessment, the student will be recommended to become a new employee of PT BUMA who comes from the vocational level through an industry-based curriculum education program. There are 5 criteria in the process of calculating the assessment of industrial class students, including the average student report card grades, psychological test results, HRD interviews, physical checks and pre-medical. Value processing uses a decision support system with a Multi-Objective Optimization method on the basis of Ratio Analysis (MOORA) where if the greater the student's grade results, the better the rankings produced, the more in line with the criteria expected by the company. The MOORA method was chosen because it can provide ease in the ranking process by finding the best alternative from several alternatives.
SIKEPUL: Sistem Informasi Untuk Administrasi Transaksi Jual Beli Pengepul Rongsokan Menggunakan Metode Waterfall Ika Dewi Rozaurrohmah; Lutfi Syafirullah; Oman Somantri
Journal of Innovation Information Technology and Application (JINITA) Vol 3 (2021): JINITA, December 2021
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1267.638 KB) | DOI: 10.35970/jinita.v3i2.670

Abstract

Currently collector businessmen are experiencing problems, namely the absence of data collection for suppliers and collapsed transaction activities. In addition, the administrative data collection process is still carried out manually by the admin, , one of which is using notes when making junk transactions and when partners make payments to collectors, there are often communication errors in junk transactions between suppliers and partners often occur. In order to overcome the existing problems, this research proposes the development of a collector administration information system named SIKEPUL using the laravel framework. The method in developing the system used is the waterfall method. The results showed that the SIKEPUL information system could solve the problems faced. The overall results of the questionnaire for 30 respondents were that 20% said it was very good, 52% said it was good, and 28% said it was enough for this system. 
Pelatihan Literasi Digital dan Similarity Check Untuk Pembuatan Karya Ilmiah Hasil Penelitian Guru Sekolah Kejuruan Oman Somantri; Musyafa Al Farizi
Madaniya Vol. 1 No. 2 (2020)
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (498.137 KB)

Abstract

Saat ini guru dihadapkan pada kondisi yang dituntut untuk dapat menguasai kemampuan kompetensi lain selain mengajar, salah satunya adalah dapat menggunakan teknologi informasi sebagai sumber informasi pengajaran serta dapat memebuat sebuah karya ilmiah dari hasil penelitian yang dilakukannya. Keadaan inilah yang saat ini menjadi permasalahan hampir semua guru, yaitu terkait dengan pemahaman dan keterampilan dalam pemanfatan teknologi informasi yang dijadikan sebagai literasi digital dan kurang pehamanan terhadap similarity check. Solusi yang disusulkan pada kegiatan pengabdian ini adalah sebuah pelatihan yang diberikan kepada guru sekolah dalam terkait dengan literasi digital dan similarity check. Metode pelaksaan pada kegiatan pengabdian ini adalah dimulai dari tahapan perencanaan, assessment permasalahan mitra, pelaksanaan kegiatan, tahapan pendampingan, dan terakhir adalah evaluasi kegiatan sebagai tolak ukut tingkat keberhasilan program. Pada kegiatan pelatihan ini para peserta kegiatan mendapat peningkatan pengetahuan yang lebih dibandingkan sebelum pelaksanaan pelitahn diberikan, hasil pelatihan menunjukan bahwa 77,9% peserta kegiatan mengatakan bahwa kegiatan pelatihan terkait dengan literasi digital dan cek similarity adalah baik.
Co-Authors Abdul Rohman Supriyono Abdul Rohman Supriyono Agus Susanto Agus Susanto Ali Sofyan Amir Hamzah Andesita Prihantara Annisa Romadloni Ari Kristiningsih Arif Wirawan Muhammad Ayu Pramita Catur Supriyanto Dairoh Dairoh Dairoh Dairoh, Dairoh Dany Artha Widiyanto Dega Surono Wibowo Dega Surono Wibowo, Dega Surono Diantono Abda’u, Prih Dodi Satriawan Dwi Wahyu Susanti Dyah Apriliani Dyah Apriliani Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Eka Tripustikasari Erna Alimudin Evila Purwanti Sri Rahayu, Theresia Fadillah Fadillah Fadlilah, Ilma Faulin, Muhammad Husni Ganjar Ndaru Ikhtiagung Ginanjar Wiro Sasmito, Ginanjar Wiro Hety Dwi Astuti Ida Afriliana Ika Dewi Rozaurrohmah Iyat Ratna Komala Johanna, Anne Karyati, Titin Khoeruddin Wittriansyah Laura Sari Lina Puspitasari Linda Perdana Wanti Linda Perdana Wanti Linda Perdana Wanti Lutfi Syafirullah Maharrani, Ratih Hafsarah Mohammad Khambali, Mohammad Muchamad Sobri Sungkar, Muchamad Sobri Muhammad Nur Faiz Musyafa Al Farizi Nur Faiz, Muhammad Nur Wachid Adi Prasetya Nurlinda Ayu Triwuri Oto Prasadi Perdana Wanti, Linda Prih Diantono Abda`u Ratih Hafsarah Maharrani Ratih Hafsarah Maharrani Ratih Hafsarah Maharrani Riyadi Purwanto Riyanto Riyanto Rohayah, Siti Santi Purwaningrum Santi Purwaningrum Sari, Laura Sasmito, Ginanjar Wiro Sena Wijayanto Taufiq Abidin Taufiq Abidin Taufiq Abidin, Taufiq Teguh Prihandoyo Titin Kartiyani Wanti, Linda Perdana Wildani Eko Nugroho Wildani Eko Nugroho, Wildani Eko Wiyono, Slamet Yeni Priatna Sari, Yeni Priatna