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All Journal Syntax Jurnal Informatika Infotech Journal Journal of Information System IJCIT (Indonesian Journal on Computer and Information Technology) Journal of Information Technology and Computer Science (JOINTECS) Techno Nusa Mandiri : Journal of Computing and Information Technology RESEARCH : Computer, Information System & Technology Management INTECOMS: Journal of Information Technology and Computer Science JUTIM (Jurnal Teknik Informatika Musirawas) JURNAL PENDIDIKAN TAMBUSAI STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Systematics Jurnal Informatika dan Rekayasa Perangkat Lunak RESLAJ: RELIGION EDUCATION SOCIAL LAA ROIBA JOURNAL Jurnal Teknologi Informatika dan Komputer Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Innovative: Journal Of Social Science Research SATIN - Sains dan Teknologi Informasi VISA: Journal of Vision and Ideas
Ultach Enri
Universitas Singaperbangsa Karawang
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Penerapan Algoritme K-Means Dalam Mengelompokkan Data Pengangguran Terbuka Di Provinsi Jawa Barat Tasyifa Nafsiah Muthmainnah; Siti Indriyana; Ultach Enri
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.8736

Abstract

Unemployment is a major social problem in many regions, including West Java province in Indonesia. West Java province is one of the most populous regions with a high level of urbanization. With population growth and urbanization, the challenge of creating enough jobs becomes more difficult. Therefore, the purpose of this study is to cluster open unemployment data in West Java communities classified by the number of unemployed people by district or city. This research uses CRISP-DM method with K-Means clustering algorithm. The result of this research is 10 regencies/cities that have low level of unemployment, then there are 15 regencies/cities that have medium level of unemployment and there are 2 regencies/cities that have high level of unemployment. The result of the test using Davies Bouldin Index cluster = 3 has the best cluster quality, because the value of the Davies Bouldin Index test result with c = 3 is the smallest value of 0.28, which is the lower, the better the cluster.
Analisis Sentimen terhadap Penyelenggaraan Sea Games 2023 Kamboja pada Twitter Menggunakan Algoritma Naive Bayes Farah Fadila Rahman; Frise Anesha Lutia; Ultach Enri
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 5, No 2 (2023): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v5i2.8946

Abstract

Southeast Asian Games or SEA Games is a Southeast Asian sporting event held every 2 years, where the participants are 11 member countries of the Association of Southeast Asian Nation (ASEAN). Cambodia was chosen as the host for the 2023 SEA Games. The implementation of the SEA Games in Cambodia experienced many controversies ranging from the inverted Indonesian flag to leaking lodging rooms for athletes. Social media Twitter became one of the places for netizens to express their opinions about the implementation of the SEA Games in Cambodia. This study aims to determine the level of tendency of positive, negative and neutral opinions through the sentiment analysis process. The sentiment analysis process is carried out using the Naive Bayes method, through five main stages, namely Data Selection, Preprocessing, Transformation, Data Mining, and Evaluation. The data used comes from Twitter users who use the hashtag "SEA Games Cambodia" then obtained data as many as 1595 tweets. The results of this study describe the results of Naive Bayes implementation and performance testing using confusion matrix obtained accuracy 66%, precision 70%, recall 66%, and f1-score 61%. and also obtained the results of the tendency of public opinion sentiment on Twitter with positive results as much as 49%, then negative results as much as 40% and neutral results as much as 11%.
Sistem Pakar Mendeteksi Penyakit Ikan Nila dengan Metode Certainty Factor Berbasis Android Rifaldi Febrianto; Oman Komarudin; Ultach Enri
Innovative: Journal Of Social Science Research Vol. 4 No. 1 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i1.8906

Abstract

Ikan dikenal luas sebagai sumber protein makanan yang berharga untuk konsumsi manusia. Ikan seringkali diperoleh melalui proses penangkapan atau budidaya. Ikan nila merupakan salah satu jenis ikan yang dapat dibudidayakan. Meskipun demikian, budidaya ikan nila mempunyai berbagai tantangan, terutama dalam identifikasi ikan nila yang terinfeksi dan karakteristik terkaitnya. Untuk memudahkan operasional para petani kolam ikan nila, maka pengembangan aplikasi yang dilengkapi dengan sistem pakar untuk mendeteksi penyakit pada ikan nila dipandang perlu. Penelitian ini melibatkan pengembangan aplikasi sistem pakar untuk Android, memanfaatkan bahasa pemrograman Kotlin dan mengimplementasikan pendekatan Certainty Factor. Penelitian ini menggunakan metodologi Expert Systems Development Life Cycle (ESDLC). Hasil dari penelitian ini memerlukan pengembangan aplikasi sistem pakar yang secara efektif mengidentifikasi penyakit pada ikan nila berdasarkan gejala yang dipilih pengguna, sekaligus menawarkan pengobatan yang sesuai untuk penyakit yang didiagnosis.
Analisis Sentimen Maxim dengan Perbandingan Chi Square dan MI pada Naive Bayes Dwinda Putri Nuria; Ultach Enri; Yuyun Umaidah
Jurnal Pendidikan Tambusai Vol. 8 No. 1 (2024): April 2024
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v8i1.14669

Abstract

Pesatnya perkembangan teknologi digital berdampak besar bagi manusia, terutama dengan meningkatnya popularitas layanan transportasi online. Saat ini penggunaan transportasi online masih didominasi oleh Gojek kemudian diikuti oleh Grab dan Maxim. Maxim adalah perusahaan transportasi online di Indonesia yang menawarkan berbagai layanan melalui aplikasinya. Meskipun menawarkan tarif yang lebih murah daripada Gojek dan Grab, persentase penggunaan Maxim masih tertinggal. Untuk mengetahui faktor penyebabnya, dilakukan analisis sentimen dari ulasan pengguna aplikasi Maxim di Google Play. Analisis sentimen ini dilakukan dengan menggunakan metodologi KDD dengan tahapan berikut: data selection, preprocessing, transformation, data mining dan evaluation. Pada proses penelitian digunakan algoritme Naive Bayes dengan seleksi fitur Chi Square dan Mutual Information untuk mengoptimalkan pengklasifikasian. Data yang digunakan merupakan data ulasan dari Google Play sebanyak 1820 data yang terdiri dari 961 data positif dan 859 data negatif. Hasil klasifikasi menggunakan algoritme Naive Bayes dengan seleksi fitur Chi Square menghasilkan tingkat akurasi terbesar yaitu 96,97%, precision 97%, recall 97%, f1-score 97% yang menghasilkan prediksi 978 data positif dan 842 data negatif.
Analisis Sentimen pada Ekspedisi Kurir Online di Indonesia Menggunakan Algoritma Naive Bayes Heri Kurniawan; Moh. Aulia Miftakhurahmat; Ultach Enri
VISA: Journal of Vision and Ideas Vol. 4 No. 3 (2024): VISA: Journal of Vision and Ideas
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/visa.v4i3.5140

Abstract

This journal focuses on sentiment analysis of four popular online courier expeditions in Indonesia, namely JNT, JNE, Shoope Express, and Anteraja. The purpose of this research is to understand the opinions and responses of users towards the online courier expedition service. The research was conducted using the sentiment analysis method, which utilizes data from user reviews contained in each online courier application on the Playstore platform. In this study, the Naive Bayes algorithm is used to perform sentiment analysis. This algorithm was chosen with the aim of producing higher accuracy results in determining positive and negative sentiments from user reviews. By using this method, this research hopes to provide a deeper understanding of the perceptions and experiences of users of the online courier service under study. The results of the study show that there are variations in user sentiment for each online courier expedition. User reviews include aspects of satisfaction with service, desired complaints, and problems that often arise when using the online courier application. These findings provide valuable insights for online courier service providers, as it can help them improve service quality and customer satisfaction. By understanding user opinions and feedback, online courier service providers can identify deficiencies in their services and make necessary improvements.
Co-Authors Adi Kurniawan Adi Santoso Agung Prabowo Amar Luthfi Azhari Ali Ridha Bayu Laksono Wahyu Arminsyah Betha Nurina Sari Chaerur Rozikin Dewi Septiani Dharma Ajie Nur Rois Dina Amelia Alzahra Dwinda Putri Nuria Elin Nurlia Farah Fadila Rahman Farid Farid Frise Anesha Lutia Hannie, Hannie Heni   Anggraeni Heri Kurniawan Heru Triana Hopi Siti Hopipah Istiningdias, Dini Sri Jaman, Jajam Haerul Kamal Prihandani Moh. Aulia Miftakhurahmat Muhammad Diki Hendriyanto Nina Sulistiyowati Nur Arifin Putri Dewi Wulandari Rifaldi Febrianto Riska Sagita Siti Indriyana Tasyifa Nafsiah Muthmainnah Tesa Nur Padilah Tia Adha Mariam Putri Tiara Destiana Yuyun Umaidah