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All Journal JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science 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 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 Systematics Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Applied Engineering and Technological Science (JAETS) JUKI : Jurnal Komputer dan Informatika Jurnal Perangkat Lunak Login : Jurnal Teknologi Komputer Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science)
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Jurnal Penelitian Pendidikan IPA (JPPIPA)

 1 
Accurately Determining Labor Test Results Using the Rough Set Method Retno Devita; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7069

Abstract

An exam is something that must be done to test a person's ability or intelligence. The laboratory exam in the Computer Systems study program at Putra Indonesia University "YPTK" Padang consists of a digital systems exam, a fuzzy logic control exam, and a tool presentation. The Labor Exam must be passed by students who will take the comprehensive exam. In this study, laboratory exam data was taken for 20 students. So far, processing of student laboratory exam results has been done manually so it takes a long time to make decisions. To overcome this problem, a Rough Set method is used to determine laboratory test results. The Rough Set method is part of machine learning. This research produces 29 rules as knowledge, namely {Digital System} Or {A} = 3 rules, {Fuzzy Logic} Or {B} = 3 rules, {Tool Presentation} Or {C} = 3 rules, {Fuzzy Logic, Tool Percentage} Or {BC} = 6 rules, {Digital System, Fuzzy Logic} Or {AB} = 6 rules and {Digital System, Tool Percentage} Or {AC} = 8 rules. The Rough Set method can determine student laboratory exam results (pass or fail) accurately.
Machine Learning Predicts the Level of Disease Spread Dhio Saputra; Irzal Arief Wisky; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7070

Abstract

The aim of the research is predictive analysis of the spread of disease. Variable analysis at the population level in a region and the total disease events detected in the community. These variables can show the accuracy and certainty of the status of the resulting analysis. The concept of Machine Learning analysis is proposed to develop previous analysis models. The methods used include the K-Means cluster, Naïve Bayes, and Decision Tree (DT). There are two stages in the analysis process: pre-processing and classification. The discussion presented by K-Means provides a classification analysis pattern. The patterns obtained will be passed on to the classification process using Naïve Bayes and DT. Naïve Bayes results provide quite significant results with an accuracy rate of 83.33%. DT can also describe the results of information and knowledge analysis in the form of decision trees. DT produces decision trees that can provide knowledge and information analysis. The DT results provide an accuracy rate of 91.76% so these results can be used as consideration in decision making. The resulting information and knowledge can be used as a guide in making policies for handling health in the community.
Prediction of Graduation Accuracy Using the K-Means Clustering Algorithm and Classification Decision Tree Sri Rahmawati; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7073

Abstract

Becoming a scholar at the right time for students is a very meaningful award for them if it is supported by seriousness and perseverance in their studies. Here, sample data was taken from 131 randomly taken in testing. Where there are still students who are not detected by the study program in completing their lectures, so research is carried out on clustering and classification with decision trees in determining the level of accuracy of lectures by clustering data, determining the initial centroid value and the centroid point. The results found were that there were 78 people grouped in cluster 0 and 53 people grouped in cluster 1, where those with potential for punctuality for their studies were in cluster 0 so they were students who could finish within the specified time. Meanwhile, students grouped in cluster 1 illustrate that these students need coaching and guidance both in the study program and with their supervisors. In the classification taken from the results of data clustering, two classes were obtained, namely class a and class b, with 73 and 58 data respectively, so that the results between clustering and classification did not differ too much in the data to predict the accuracy of a student's graduation.
Rought Set: Effective Method for Determining Scholarship Recipients Silfia Andin; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7088

Abstract

Every year, higher education institutions receive a KIP Tuition scholarship quota that has been determined by Ristek Dikti through LLDIKTI which is given during the new student admissions process. The process of determining recipients is carried out manually resulting in inaccurate scholarship recipients being selected and the selection results may not be the same based on those who participated in making the decision. This research is motivated by the need for an algorithm for determining prospective scholarship recipients that is appropriate and effective because the recipient selection process often takes a long time because many high school and equivalent students register so that they exceed the quota limit while the quota given is limited. This research aims to use a system for scholarship recipients and provide rules and knowledge, namely rough set Theory and adapted to the Rosetta application, using prospective student data during the selection process for new students who apply for the KIP Kuliah scholarship in the 2020/2021 academic year. The resulting decision is the KIP Opportunity which consists of 4 (four) attributes, including parents' income, housing status, dependents, and parental status. The results of this research using sample data from 12 people produced 6 (six) rules and knowledge of 26 rules. This research is very supportive in identifying the eligibility of KIP Kuliah recipients.
Development of the Rough Set Method to Determine Lecturer Scholarship Opportunities Surmayanti; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 5 (2024): May
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i5.7147

Abstract

Currently, all groups can experience the development of artificial intelligence, this happens because artificial intelligence has experienced very significant changes. Artificial Intelligence (AI) consists of several branches, one of which is machine learning. Machine Learning (ML) technology is a branch of AI that is very interesting because it is a machine that can learn like humans. The method used here is the rough set method. In this research, a case will be raised to determine scholarship opportunities for lecturers based on predetermined criteria. To solve the problem above, machine learning was used using the Rough Set method, using Rosetta software. By the regulations determined by the scholarship provider, in this case, the institution concerned where the lecturer is registered as teaching staff to obtain a scholarship, criteria are needed to determine who will be selected to receive the scholarship. The distribution of scholarships is carried out to improve lecturer performance, as an achievement as well as an appreciation for the lecturer concerned for his long service to the institution.
Co-Authors Abdul Azis Said Adek Putri Adi Gunawan Adi Gunawan, Adi Agung Ramadhanu Agus Perdana Windarto Ahmad Zamsuri, Ahmad Am, Andri Nofiar Amran Sitohang Andri Nofiar Angga Putra Juledi Anggrawan, Anthony ardialis Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bisma Okmarizal Bosker Sinaga Daeng Saputra Perdana Daniel Theodorus Dayla May Cytry Dendi Ferdinal Deno Yulfa Ardian Dhena Marichy Putri Dhio Saputra Dinda Permata Sukma Dwi Utari Iswavigra Dwiki Aulia Fakhri Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma eriwandi Fadlul Hamdi Faisal Roza Fanny Septiani Bufra Fauzan Azim Fauzi Erwis Febri Aldi Febri Hadi Febrina, Yerri Kurnia Fitriani, Yetti Fristi Riandari Fristi Riandari Fuad El Khair Gunadi Nurcahyo Gunadi Widi Nurcahyo Habdi Habdi Halifia Hendri Handika, Yola Tri Haris Kurniawan Hasmaynelis Fitri Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Indah Savitri Hidayat Ira Nia Sanita Irzal Arief Wisky Ismail Virgo Jefdy Kurniawan Jeri Wandana Juansen, Monsya Juledi, Angga Putra Khairul Azmi Kurniawan, Jefdy L. J. Muhammad Larissa Navia Rani Leoni Lidya M Syahputra M. Ibnu Pati Mardayatmi, Suci Mardison Mardison Mardison Meilinda Sari Meilinda Sari Melissa Triandini Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen MUHAMMAD TAJUDDIN Nadya Alinda Rahmi Nandel Syofneri Nanik Istianingsih Nopi Purnomo Nori Sahrun, Nori Novi Yanti Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhidayat Pati, Muhammad Ibnu Putra, Rahman Arief Putri, Adek R Rahmiyanti Rafika Sani Rafiska, Rian Rahmad Aditiya Rahman Arief Putra Ramadhan, Mukhlis Ramdani Bayu Putra Retno Devita Rezki - Rezki Rusydi Rian Kurniawan Rianti, Eva Rio Andika Malik Ritna Wahyuni Riyan Ikhbal Salam Rizki Mubarak Rusdianto Roestam S Sumijan Salam, Riyan Ikhbal Sandrawira Anggraini Sandy Mulyanda Setiawan, Adil Shahab Wahhab Kareem Sharon Shaza Alturky Silfia Andin Sirait, Weri Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sri Dewi Sri Dewi Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Surmayanti Surya Dwi Putra Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syahputra, M Syaljumairi, Raemon Virgo, Ismail Vivi Suryani Wahyuni, Ritna Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yuli Hartati Yunus, Yuhandri Yusma Elda Zulvitri, Z Zurni Mardian