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All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining Rang Teknik Journal ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Systematics Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Manajemen Kesatuan Dinasti International Journal of Digital Business Management JUKI : Jurnal Komputer dan Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Penelitian Inovatif Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Innovative: Journal Of Social Science Research e-Jurnal Apresiasi Ekonomi Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
Metode k-means clustering untuk mengukur tingkat kedisiplinan pegawai (studi kasus di pemerintah kabupaten padang pariaman) Rezki -; Sarjon Defit; Sumijan
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4728

Abstract

Knowledge Discovery In Database (KDD) is a process of converting raw data into useful data in the form of information. Data mining is a technique of digging up hidden or hidden valuable information in a very large data collection (database) so that an interesting pattern is found that was previously unknown. Clustering is a method in data mining in which data objects that have similarities or the same characteristics are grouped into one group and those that are different are grouped into another group. One aspect of discipline that can be used to evaluate employee performance is attendance. The k-means method is used to classify employee discipline levels and then describes the values ​​that have been obtained to generate new knowledge regarding data patterns on employee discipline levels. The attendance data is clustered into 3, namely to measure low, medium, and high levels of discipline. After carrying out the calculation process, the 41 employee samples produced 3 iterations, and the final result was 3 clustering, namely cluster 1 of 10 employees with low discipline, cluster 2 of 7 employees with moderate discipline, and cluster 3 of 24 employees with high discipline. This is intended so that leaders can find out which employees have high, medium and low levels of discipline so that they can provide appreciation or rewards and sanctions in order to maintain and improve their discipline so that service to the community can be optimal and the vision and mission of the local government can be achieved. Keywords: KDD, Data Mining, K-Means Clustering Method, Discipline
Sistem Pendukung Keputusan Menggunakan Metode Multi Attribute Utility Theory Untuk Pemilihan Layanan Digital Ira Nia Sanita; Sarjon Defit; Gunadi Widi Nurcahyo
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4742

Abstract

Dinas Komunikasi, Informatika dan Statistik (Kominfotik) Provinsi Sumatera Barat merupakan Dinas yang diberi kewenangan untuk membangun dan mengembangkan layanan digital untuk semua Perangkat Daerah di Pemerintah Provinsi Sumatera Barat. Seluruh Perangkat Daerah dapat mengajukan permintaan pembangunan layanan digital ke Dinas Kominfotik. Akan tetapi, tidak semua layanan digital yang diminta akan difasilitasi dan diakomodir oleh Dinas Kominfotik. Ada beberapa kriteria pemilihan dalam pembangunan Layanan Digital yaitu Layanan Digital yang sesuai dengan Arsitektur Sistem Pemerintahan Berbasis Elektronik (SPBE) Nasional, mendukung Program Unggulan Pemerintahan Provinsi Sumbar, Quick Win Layanan sesuai Peta Rencana SPBE, tujuan pembuatan layanan digital, serta Bahasa Pemograman yang digunakan dalam pembangunan Aplikasi. Penelitian ini menggunakan metoda Multi Attribute Utility Theory (MAUT). Metode MAUT digunakan untuk menentukan pemilihan layanan digital yang akan dibangun berdasarkan bobot dan kriteria yang sudah ditentukan. Kemudian dilakukan proses perankingan yang akan menentukan pilihan yang menjadi prioritas. Dan dari hasil pengujiannya didapatkan penerapan metode MAUT pada Sistem Pendukung Keputusan pemilihan layanan digital menghasilkan alternatif yang menjadi prioritas (rangking 1) adalah Layanan Penerimaan Peserta Didik Baru (PPDB) dengan nilai 0,933. Kata Kunci : Sistem Pendukung Keputusan, Layanan Digital, Multi Attribute Utility Theory (MAUT)
Metode Multi Attribute Utility Theory (MAUT) Untuk Penilaian Kinerja Guru Yamin, Abdul Yamin; Defit, Sarjon; Sumijan, Sumijan
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.5920

Abstract

The performance assessment of teachers is a foundation or basis for the development decisions in terms of promotion and career of teachers in a madrasah or school. Currently, teacher performance assessment at Pondok Pesantren MTI Canduang is limited to teachers who are civil servants (PNS) or have obtained certification. In an effort to improve the quality of education, it is important to evaluate the performance of all teachers, including those who are not civil servants. The conventional method of assessment using paper-based evaluation sheets is considered inaccurate and inefficient due to the large number of teachers being assessed. Furthermore, there is no appropriate method for making decisions regarding teacher reward programs. Therefore, the purpose of this research is to apply the Multi Attribute Utility Theory (MAUT) method for teacher performance assessment. This method aims to provide a basis for decision-making in recommending teachers who deserve rewards in each assessment period. Based on the test results using the MAUT method with 40 teacher data and 12 defined assessment criteria, it was found that 3 data points for Tsanawiyah level had the highest value of 0.797 and the lowest value of 0.332, while 3 data points for Aliyah level had the highest value of 0.874 and the lowest value of 0.386. Thus, the research results can help the madrasah determine the best alternatives according to predefined criteria and weights. The resulting web-based application can facilitate the assessment process by making it easier, faster, and more accurate.
Implementasi Naïve Bayes dalam M-Series 4 Mobile Legends untuk Prediksi Kemenangan Tamaza, Muhammad Abyanda; Defit, Sarjon; Sumijan, Sumijan
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6707

Abstract

Mobile Legends is a game made by a developer from China called Moontoon which implements the Multiplayer Online Battle Arena (MOBA) system which is currently popular. The popularity of this game is proven by the holding of low, middle and high level tournaments. Recently a high level or international tournament called the M-Series World Championship was held in Indonesia. This game is played by two teams consisting of five players with the aim of destroying enemy targets in the form of towers. The problem in this game is winning and losing. One of the factors that determines victory or defeat is the choice of hero. The wrong hero composition during the draft pick stage can make it difficult for your team to play and lead to unexpected results. This research aims to predict the percentage level of Mobile Legends wins based on the drafted heroes. Prediction is the process of minimizing errors in systematically estimating the future based on past information. The technique used in this research is the Naïve Bayes algorithm. The Naïve Bayes algorithm is a classification method based on probability. This method consists of four stages, namely data understanding, data preparation, data analysis, and results analysis. This research dataset is provided by Youtube MPL Indonesia. The dataset consists of 880 training data and 90 test data for M-Series 4 Mobile Legends. The results of this research provide a percentage value in the form of prediction of 96.67%, precision of 95.65% and recall of 97.78%. The results of an accuracy rate of 96.67% using the Naïve Bayes algorithm show that predictions using the Naïve Bayes algorithm can be applied to predict win ratios in M-Series 4 Mobile Legends.
Co-Authors Abdul Azis Said Adawiyah, Quratih Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis, Adyanata Afriyadi, Iqbal Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zamsuri, Ahmad Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andin, Silfia Andri Nofiar Angga Putra Juledi Anthony Anggrawan Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Bosker Sinaga Breinda, Engla Bufra, Fanny Septiani Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devita, Retno Dhena Marichy Putri Dila, Rahmah Dinda Permata Sukma Dwi Utari Iswavigra Dwiki Aulia Fakhri Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma eriwandi Fadlul Hamdi Faisal Roza Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus, Muhammad Bambang Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra, Guslendra Hadiyanto, Tegas Halifia Hendri Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendrik, Billy Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Hidayat, Rahmadani Honestya, Gabriela Huda, Ramzil Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arief Wisky Ismail Virgo Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Juledi, Angga Putra Junadhi, Junadhi Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Syahputra Malik, Rio Andika Mardayatmi, Suci Mardison Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Menhard, Menhard Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen MUHAMMAD TAJUDDIN Muhammad, L. J. Mulyanda, Sandy Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun, Nori Novi Yanti Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Parinduri, Rezti Deawinda Pati, Muhammad Ibnu Pebriyanti, Defi Pratiwi, Mutiana Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rahmad Aditiya Rahmad Rahmad Rahman Arief Putra Rahmi, Nadya Alinda Ramadhan, Mukhlis Ramadhanu, Agung Ramdani Bayu Putra Rani, Larissa Navia Refina Afindania, Pipin Resnawita, R Rezki - Rezki Rusydi Rian Kurniawan Rianti, Eva Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Rusdianto Roestam Rustam, Camila S Sumijan Said, Abdul Azis Sandrawira Anggraini Sani, Rafikasani Saputra, Dhio Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Siregar, Diffri Solihin Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Suri, Ghea Paulina Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki, Syafrika Deni Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva Tukino, Tukino Veri, Jhon Veza, Okta Virgo, Ismail Vitriani, Vitriani Wahyu, Fungki Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yamin, Abdul Yamin Yemi, Leonardo Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yulasmi Yulasmi, Yulasmi Yuli Hartati Yulihartati, Sandra Yunus, Yuhandri Yusma Elda Zakir, Supratman Zia Rahimi, Hadisha Zulvitri, Z Zuqron, M. Iqbal Zurni Mardian