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All Journal International Journal of Evaluation and Research in Education (IJERE) JURNAL SISTEM INFORMASI BISNIS Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah Flash Belantika Pendidikan Jurnal Manajemen Informatika Jayakarta HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Indonesian Journal of Educational Development (IJED)
S Sumarlin
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences

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Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM Sumarlin, Sumarlin
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 5, No 1 (2015): Volume 5 Nomor 1 Tahun 2015
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1677.37 KB) | DOI: 10.21456/vol5iss1pp52-62

Abstract

In line with the growth in the academic field especially college, scholarship is a problem that is interesting to study. Several studies in the field of computers for the screening or classification scholarships have been carried out in the academic authorities to minimize the error in awarding scholarships. This study discusses the classification of PPA and BBM scholarships based on variables that have been determined by applying the k-nearest neighbor algorithm. The process of selecting awardees PPA and BBM requires a decision support system (DSS) to help provide alternative solutions. The results of the classification system will be used as a decision in awarding scholarships to students who submit. Results of testing to measure the performance of k - nearest neighbor algorithm using cross validation method, Confusion Matrix and the Receiver Operating Characteristic (ROC) curve, the accuracy obtained for PPA scholarships reached 88.33% with a value of 0.925 area under curve (AUC)  dataset of 227 records, while accuracy is obtained for fuel BBM scholarships reached 90% with a value of  0.937% AUC dataset of 183 records​​, accuracy for PPA and BBM scholarships reached 85,56% and AUC value 0,958. Because AUC values ​​were in the range of 0.9 to 1.0  the method falls within the category of very good (excellent). Keywords: Decision Support System; K-nearest neighbor; Classification; Scholarship
KONSEP DAN RANCANGAN LAYANAN ALTERNATIF BERBAGI MODUL PENGAJARAN DAN PEMBELAJARAN DIGITAL KAMPUS Edwin Ariesto Umbu Malahina; S Sumarlin
Jurnal Ilmiah Flash Vol 6 No 2 (2020)
Publisher : P3M- Politeknik Negeri Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32511/flash.v6i2.713

Abstract

Penelitian ini merupakan penalaran konsep dalam bentuk rancanga design system untuk berbagi modul pengajaran, dimana konsep ini, akan membutuhkan kerjasama dan persetujuan Mitra Kampus dalam berkolaborasi dan berpartisipasi dalam menyusun serta melampirkan cetakan digital file modul digital kedalam system yang akan dikembangkan nantinya. Model system ini menerapkan model waterfall, karena model ini memiliki tahap yang jelas dan sesuai dengan keinginan system yang dikembangkan kedepannya, proses yang dimiliki model ini adalah; analysis, design, implementation, testing, deployment dan maintenance. Pengembangan system berbagi modul ini dapat dijalankan pada aplikasi mobile Android dan web browser baik untuk admin dan client (Mitra Kampus). Konsep ini, diharapkan agar mendorong dan memberikan dampak baik bagi dunia kampus dalam menerapkan system sharing dan update knowledge, sehingga keilmuan, pembelajaran dan kompetensi keahlian setiap tenaga pengajar dapat selalu beradaptasi dengan terapan ilmu dari berbagai kampus-kampus lainnya sehingga tidak ketertinggalan dalam belajar hal-hal baru, serta ilmu yang didapatkan dari kampus-kampus yang bereputasi baik dengan tenaga pengajar yang berkopetansi serta ahli dibidangnya, akan menambah wawasan lebih luas dengan penerapan haring dan update knowledge
Pengembangan Media Pembelajaran Adaptif Berbasis Fuzzy Expert System untuk Meningkatkan Prestasi Belajar Siswa Sumarlin Sumarlin
Belantika Pendidikan Vol 4, No 1 (2021)
Publisher : Kayon Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47213/bp.v4i1.105

Abstract

Beberapa peneliti telah menunjukkan pentingnya memberikan bimbingan dan dukungan untuk belajar secara individu. Dalam beberapa dekade terakhir, sebagian besar penelitian tentang bagaimana cara mengembangkan sistem pembelajaran adaptif untuk mengatasi masalah ini terutama berdasarkan status kognitif siswa, seperti prestasi belajar. Namun, beberapa pendidik telah menunjukkan perlunya mempertimbangkan status afektif peserta didik. Oleh karena itu, penelitian ini mengusulkan pendekatan sistem pakar fuzzy dengan mempertimbangkan status afektif dan kognitif individu peserta didik. Sistem pembelajaran adaptif dilaksanakan berdasarkan pendekatan yang diusulkan. Selain itu, perlu dilakukan percobaan pada salah satu mata pelajaran untuk membandingkan prestasi belajar dan persepsi siswa yang melakukan pembelajaran dengan sistem pembelajaran adaptif dengan analisis status afektif dan kognitif.  Selain itu, berdasarkan hasil penelitian ditemukan bahwa aplikasi yang dikembangkan membantu siswa yang berprestasi rendah berhasil menyelesaikan tugas-tugas pembelajaran, sementara mereka yang belajar dengan pendekatan berbasis faktor kognitif konvensional lebih cenderung menyerah dalam menyelesaikan beberapa tugas pembelajaran, dan lebih bergantung pada versi rinci dari bahan ajar.
SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN PEMENANG LELANG TENDER PROYEK MENGGUNAKAN METODE PERBANDINGAN EKSPONENTIAL PADA KANTOR DINAS PEKERJAAN UMUM TIMOR TENGAH UTARA Deni Alfiando Salem; S Sumarlin
Jurnal Ilmiah Flash Vol 8 No 1 (2022)
Publisher : P3M- Politeknik Negeri Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32511/flash.v8i1.949

Abstract

Tenders or auctions in the construction industry are often held, either public or open or closed depending on the owner and the project to be carried out. Tenders or auctions are needed because in the implementation of a project, both government projects and private projects, the owner expects the best bid for the project. A tender committee is an organization established and endorsed which is responsible for the successful auction of a tender. The tender or auction in the north middle Timor regency is held every year after the local government proposes the project procurement in the budget session with the DPR. Project procurement is proposed at the hearing in the form of the location and amount of the project budget, during the budget session. The amount of budget proposed by the government can be reduced and the project procurement can be canceled unilaterally by the DPR, after the budget session is over and the project procurement has been approved, the project will be posted on the LPSE (Electronic Procurement Service) website of north middle Timor regency. In determining the tender winner, at the final determination stage, is still done manually so there are a lot of cheats and frequent human errors, therefore an application is needed to assist and facilitate decision-making in determining the winner of the project tender. This research produces a decision support system application to determine the winner of the project tender that can assist the committee in determining decision-making using the Exponential Comparison Method. The result of the system implementation is that the system can assist and facilitate decision-making in determining the project tender winner. The decision support system for determining the winner of this project tender can assist the committee in determining decision-making using the Exponential Comparison Method. Measuring the accuracy of determining the project tender winner between the Public Works Agency ranking with the Exponential Comparison Method (MPE) algorithm ranking, obtained an accuracy rate of 100%, so that the Exponential Comparison Method algorithm is feasible to be implemented in making a decision support system for determining project tender winners at the office North Middle Timor Regency Public Works.
Improvement of engineering student’s learning outcomes in high schools using adaptive educational hypermedia system Sumarlin, Sumarlin; Setyosari, Punaji; Ulfa, Saida; Degeng, Made Dunanda Kartika
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i5.28381

Abstract

This was a research and development (R&D) which aims to develop adaptive educational hypermedia system (AEHS) learning media. The use of AEHS based on learning style in supporting the online learning process is considered very effective for use by engineering students because it can be accessed via mobile devices which can make it easier for students to learn and has an effect on increasing learning outcomes, this is supported by several inputs from experts through expert learning design tests, learning instrument experts, learning media experts and learning outcome measurement experts with the assessment results included in the very good category. The participants in this study were informatics engineering students, totaling 100 students. Small group tests were conducted for participants and obtained a gain score of 0.735 included in the 'high' category. The pretest and posttest have been carried out and the results show that the average posttest score is greater than the pretest value. A comparison between the use of AEHS developed with web-based learning was carried out and it can be concluded that the use of AEHS based on learning styles further improves student learning outcomes in informatics engineering compared to web-based learning.
PEMBELAJARAN ADAPTIF BERBASIS SISTEM CERDAS UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR KRITIS MAHASISWA DI PERGURUAN TINGGI Sumarlin; Naatonis, Remerta Noni; Anggraini, Dewi
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 15 No. 2 (2024): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol15no2.p136-145

Abstract

Penelitian ini bertujuan untuk menganalisis penerapan pembelajaran adaptif berbasis sistem cerdas dalam meningkatkan keterampilan berpikir kritis mahasiswa dalam proses pembelajaran online selama perkuliahan. Pembelajaran adaptif dapat menyediakan konten materi kuliah yang sesuai dengan karakteristik dan gaya belajar mahasiswa secara mandiri. Pembelajaran adaptif berbasis sistem cerdas yang dikembangkan mampu mendeteksi gaya belajar mahasiswa sesuai dengan hasil kuesioner VARK sebagai dasar untuk mengenali karakteristik mahasiswa, serta dapat merekomendasikan konten pembelajaran yang sesuai dengan gaya belajar mahasiswa. Dengan demikian, mahasiswa diharapkan dapat meningkatkan keterampilan berpikir kritis yang akan berdampak pada peningkatan hasil belajar. Untuk menguji efektivitas sistem yang dikembangkan, digunakan uji T Independen dengan membagi sampel 100 menjadi dua kelompok (eksperimen kuasi), yaitu kelas eksperimen dan kelas kontrol. Hasil penelitian menunjukkan bahwa terdapat pengaruh yang sangat signifikan antara hasil kelas eksperimen dan kelas kontrol dengan nilai signifikansi (P=0,020) <(0,050), sehingga dapat disimpulkan bahwa sistem adaptif yang dikembangkan berjalan dengan baik, terlihat dari nilai rata-rata pada setiap kelas dimana kelas eksperimen memperoleh nilai rata-rata lebih tinggi dari kelas kontrol.   This study aims to analyze the application of intelligent system-based adaptive learning in enhancing students' critical thinking skills in the online learning process during lectures. Adaptive learning can provide lecture material content that suits the characteristics and learning styles of students independently. The intelligent system-based adaptive learning developed is capable of detecting student learning styles according to the results of the VARK questionnaire as a basis for recognizing the characteristics of these students, as well as being able to recommend learning content according to student learning styles. So that in the end students can improve critical thinking skills which will have an impact on improving learning outcomes. To test the effectiveness of the system being developed, the Independent T test was used by dividing a sample of 100 into two groups (quasi-experiments), namely the experimental class and the control class. The results showed that there was a very significant effect between the results of the experimental class and the control class with a significant value (P=0.020) <(0.050), so it can be concluded that the adaptive system developed went well, this can be seen from the average value on each class where the experimental class obtained an average score higher than the control class.
ANALISIS PERBANDINGAN KLASIFIKASI SUPERVISED DAN UNSUPERVISED CITRA SATELIT LANDSAT UNTUK PEMETAAN PENUTUPAN LAHAN DI KABUPATEN KUPANG Natun, Natasya Chalista Imanuela; Sumarlin, Sumarlin
Jurnal Manajamen Informatika Jayakarta Vol 5 No 2 (2025): Jurnal Manajemen Informatika Jayakarta (JMI Jayakarta)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v5i2.1812

Abstract

Tutupan lahan adalah representasi visual dari vegetasi, unsur alam, dan elemen buatan di permukaan bumi yang penting dalam perencanaan dan pengembangan wilayah. Di Kabupaten Kupang, perubahan signifikan pada tutupan lahan berdampak pada pengelolaan sumber daya alam dan lingkungan. Penelitian ini bertujuan membandingkan dua metode klasifikasi citra satelit, yaitu Maximum Likelihood Classification sebagai metode supervised dan ISODATA sebagai metode unsupervised, untuk memetakan tutupan lahan menggunakan citra Landsat 9 OLI-2/TIRS-2. Proses klasifikasi menghasilkan delapan kelas tutupan lahan. Untuk memvalidasi hasil klasifikasi, dilakukan verifikasi lapangan (groundcheck) untuk memastikan kesesuaian dengan kondisi di lapangan. Hasil penelitian menunjukkan bahwa dari segi akurasi, metode Supervised Classification pada algoritma MLC memiliki keunggulan dengan nilai akurasi keseluruhan sebesar 96,47%, lebih tinggi dibandingkan metode Unsupervised Classification algoritma ISODATA, yang mencapai 94,2%. Meskipun ISODATA menghasilkan akurasi yang cukup tinggi, metode MLC lebih unggul dalam ketelitian yang lebih baik dalam pemetaan tutupan lahan.
DATA MINING PENDIDIKAN: PREDIKSI GAYA BELAJAR MAHASISWA TEKNIK MENGGUNAKAN MACHINE LEARNING Sumarlin, Sumarlin; Anggraini, Dewi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025129190

Abstract

Dalam platform online, pembelajar yang berbeda memiliki gaya belajar yang berbeda berdasarkan perilaku belajar. Oleh karena itu, menganalisis perilaku dan mendeteksi gaya belajar mahasiswa adalah penting untuk memberikan rekomendasi sumber daya yang tepat, sehingga meningkatkan hasil belajar mahasiswa. Untuk memprediksi gaya belajar mahasiswa, dihitung dan dibandingkan kinerja algoritma pembelajaran mesin seperti regresi logistik, pohon penentuan, K-Nearest neighbour, support vector machine, neural network, dan Naive Bayes. Dataset terdiri dari seratus mahasiswa teknik yang belajar Arsitektur Komputer selama satu semester. Studi berbasis data seperti ini sangat penting untuk membangun sistem analisis pembelajaran di institusi pendidikan tinggi dan membantu proses pengambilan keputusan. Hasilnya menunjukkan bahwa model yang disarankan mencapai akurasi klasifikasi sebesar 65–78% dengan hanya empat parameter digunakan: nilai akhir, predikat, program studi, dan jenis kelamin.  Hasil menunjukkan bahwa algoritma K-Nearest Neighbour memiliki tingkat akurasi 78% tertinggi dibandingkan dengan algoritma machine learning lainnya. Ini menunjukkan bahwa ada korelasi yang signifikan antara data aktual dan data prediksi. Hasilnya menunjukkan bahwa 78% sampel diklasifikasikan dengan benar.  Hasil empiris dari penelitian ini memungkinkan pemahaman yang lebih baik tentang proses penggalian data pendidikan perguruan tinggi saat ini. Pemahaman ini dapat digunakan untuk mempertimbangkan faktor-faktor yang perlu dipertimbangkan oleh para mahasiswa teknik saat membuat keputusan tentang proses pembelajaran.   Abstract In online platforms, different learners have different learning styles based on learning behavior. Therefore, analyzing behavior and detecting student learning styles is important to provide appropriate resource recommendations, thereby improving student learning outcomes. To predict student learning styles, the performance of machine learning algorithms such as logistic regression, determination trees, K-Nearest neighbors, support vector machines, neural networks, and Naive Bayes are calculated and compared. The dataset consists of one hundred engineering students studying Computer Architecture for one semester. Data-based studies like this are essential for building learning analytics systems in higher education institutions and aiding decision-making processes. The results show that the proposed model achieves a classification accuracy of 65–78% with only four parameters used: final grade, predicate, study program, and gender.  The results show that the K-Nearest Neighbor algorithm has the highest accuracy rate of 78% compared to other machine learning algorithms. This shows that there is a significant correlation between the actual data and the predicted data. The results show that 78% of the samples were classified correctly.  The empirical results of this research enable a better understanding of the current process of mining higher education education data. This understanding can be used to consider factors that engineering students need to consider when making decisions about the learning process.
Modelling user acceptance of personalised learning apps in high schools using the SEM approach Heni; Sumarlin; Naatonis, Remerta Noni; Snae, Menhya; Latuan, Yosep Jacob; Anggraini, Dewi
Indonesian Journal of Educational Development (IJED) Vol. 6 No. 3 (2025): November 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59672/ijed.v6i3.4807

Abstract

This research addresses the urgent need to understand user acceptance of personalised mobile learning applications in higher education, especially as digital learning becomes increasingly essential in post-pandemic education. The study employs a quantitative research design, utilising the Technology Acceptance Model 3 (TAM3) as the theoretical framework and Structural Equation Modelling (SEM) for analysis. The population comprises undergraduate students from various departments at STIKOM Uyelindo Kupang, selected using stratified random sampling to ensure representation across faculties. Data was collected through a validated questionnaire based on TAM3 constructs, and the instrument's validity and reliability were confirmed using Cronbach's Alpha, Composite Reliability (CR), and Average Variance Extracted (AVE). The results show that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) significantly influence Behavioural Intention (BI), while Social Influence (SI) and Facilitating Conditions (FC) also play important roles. Perceived Enjoyment (PE) enhances engagement, and Computer Anxiety negatively affects ease of use. The study concludes that TAM3 effectively models user acceptance in this context. Recommendations include improving app usability, providing institutional support, and designing engaging learning experiences to enhance the adoption and continued use of mobile learning technologies.
Co-Authors Degeng, Made Dunanda Kartika Deni Alfiando Salem Dewi Anggraini Dewi Anggraini Edwin Ariesto Umbu Malahina Heni Latuan, Yosep Jacob Menhya Snae Natun, Natasya Chalista Imanuela Punaji Setyosari Remerta Noni Naatonis Saida Ulfa