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All Journal Gaung Informatika Jurnal Teknik Komputer AMIK BSI JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Ilmu Manajemen (JIMMU) JURISMA: Jurnal Riset Bisnis & Manajemen Jambura Journal of Educational Management Jurnal Informatika dan Rekayasa Elektronik Altasia : Jurnal Pariwisata Indonesia Dinasti International Journal of Management Science Infotek : Jurnal Informatika dan Teknologi International Journal Of Science, Technology & Management (IJSTM) Jurnal Kajian Ilmiah Journal of Industrial Engineering & Management Research (JIEMAR) HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi IMTechno: Journal of Industrial Management and Technology SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Administrasi Kantor Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Paradigma International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Journal of Innovative and Creativity
Sinta Rukiastiandari
Universitas Bina Sarana Informatika
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Predicting Graduation Outcomes: Decision Tree Model Enhanced with Genetic Algorithm Rukiastiandari, Sinta; Rohimah, Luthfia; Aprillia, Aprillia; Mutia, Fara
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3165

Abstract

This research aims to improve the accuracy of predicting student permit results in the digital era by utilizing machine learning techniques. The main focus is the use of a Decision Tree (DT) model optimized with a Genetic Algorithm (GA) to overcome the limitations of accuracy and testing of conventional methods. This research began with collecting student academic data, followed by preprocessing to eliminate incompleteness and organize the data format. The DT model is then built and optimized with GA, which is inspired by biological evolutionary processes to improve feature selection and parameter tuning. The results show a significant increase in prediction accuracy, from 86.19% to 87.68%, and an increase in the Area Under Curve (AUC) value from 0.755% to 0.788%. This research not only proves the effectiveness of GA integration in improving DT models, but also paves the way for the application of evolutionary techniques in educational data analysis and other fields. The main contributions of this research include the development of more accurate prediction models and practical applications in educational contexts, with the hope of assisting educational institutions in making more informed decisions for their students.
PREDIKSI STATUS AKADEMIK MAHASISWA BERDASARKAN DATA PEMBAYARAN DENGAN NAIVE BAYES DAN PARTICLE SWARM OPTIMIZATION Rukiastiandari, Sinta; Rohimah, Luthfia; Mutia, Fara; Aprillia; Chodidjah, Chodidjah
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 2 (2025): JIRE November 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i2.1756

Abstract

Pendidikan tinggi di Indonesia menghadapi tantangan dalam pengelolaan pembayaran mahasiswa, di mana keterlambatan dapat berdampak pada status akademik, termasuk risiko cuti atau pengunduran diri. Penelitian ini bertujuan mengembangkan model prediksi status akademik berbasis data pembayaran kuliah dengan metode Naive Bayes (NB) yang dioptimasi menggunakan Particle Swarm Optimization (PSO). Dataset berjumlah 15.697 record mahasiswa yang telah melalui pra-pemrosesan, termasuk penanganan nilai hilang dan pengkodean atribut kategorikal. Hasil menunjukkan bahwa model NB menghasilkan akurasi 98,83%, precision 98,21%, recall 65,09%, dan AUC 0,905. Optimasi dengan PSO meningkatkan recall menjadi 65,13% dan AUC menjadi 0,907, sementara akurasi dan precision tetap stabil. Analisis fitur mengindikasikan bahwa Jenis Kelamin, Jurusan SLTA, dan Kuliah Sambil Bekerja merupakan atribut paling berpengaruh, sedangkan Pekerjaan Ayah relatif kurang signifikan. Temuan ini menegaskan potensi NB-PSO sebagai pendekatan prediktif untuk mendukung pengelolaan administrasi akademik yang lebih efektif.
PENGELOLAAN PRODUKTIVITAS KERJA KARYAWAN PADA INDUSTRI GARMEN MELALUI LOYALITAS KERJA, STRES KERJA DAN SUPERVISOR SUPPORT Suhardoyo, Suhardoyo; Rukiastiandari, Sinta; Hairo Rahayu, Eneng Iviq
Jurnal Ilmu Manajemen (JIMMU) Vol. 7 No. 1 (2022)
Publisher : Magister Manajemen Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jimmu.v7i1.14740

Abstract

AbstrakSetiap organisasi dan perushaan pengelolaan karyawan perlu dilakukan secara baik terutama dalam meningkatkan produktivitas kerja karyawan sehingga keberlangsungan hidup perusahaan dapat dipertahankan. Namun tidak sedikit  perusahaan mengabaikan hal ini, akibatnya  produktivitas kerja karyawan  turun  yang  mengakibatkan kerugian perusahan. Berdasarkan hasil penelitian pada industri garmen PT.Jinny Young Indonesia di Jakarta dengan sampel 66 karyawan . Berdasarkan hasil  olah data dengan program SPSS 23 untuk melihat pengaruh variabel loyalitas kerja stres kerja dan supervisor support   terhadap produktivitas kerja  karyawan yang peroleh persamaan Y = 82,201 + 0,575X1 – 0,416X2+ 0,432X3 Hal ini menunjukkan koefisien produktivitas karyawan besarnya adalah 82,201.Variabel loyalitas kerja memiliki pengaruh yang positif dan signifikan dengan nilai  0,575.Stress kerja memiliki pengaruh yang negatif dan signifikan dengan nilai - 0,416.Supervisor support  memiliki pengaruh positif dan signifikan dengan nilai 0,432. Sedangkan pengaruh secara simultan diperoleh nilai untuk  variabel  loyalitas kerja   (X1) , stress kerja (X2 ) dan  supervisor support (X3) adalah 20.345 dengan tingkat signifikansi 0,002. Untuk nilai R square sebesar 0.722 atau sebesar 72,2%..Pengelolan yang baik terhadap loyalitas kerja, stress kerja dan supervisor support  maka akan meningkatkan produktivitas kerja karyawan sehingga tujuan organisasi dapat tercapai. Kata Kunci : Loyalitas kerja, Stres kerja, Supervisor support, Produktivitas kerja AbstractEvery organization and company employee management needs to be done well. especially in increasing employee productivity so that the company's survival can be maintained. However, not a few companies ignore this, as a result employee work productivity decreases which results in company losses. Based on the results of research on the garment industry PT. Jinny Young Indonesia in Jakarta with a sample of 66 employees. Based on the results of data processing with the SPSS 23 program to see the effect of work loyalty variables, work stress and supervisor support on employee work productivity, the equation Y = 82.201 + 0.575X1 – 0.416X2+ 0.432X3 This shows the employee productivity coefficient is 82.201. The work loyalty variable has a positive and significant effect with a value of 0.575. Work stress has a negative and significant effect with a value of -0.416. Supervisor support has a positive and significant effect with a value of 0.432. Meanwhile, the simultaneous effect obtained for the variables of work loyalty (X1), work stress (X2) and supervisor support (X3) is 20,345 with a significance level of 0.002. For the value of R square is 0.722 or 72.2%. Good management of work loyalty, work stress and supervisor support will increase employee productivity so that organizational goals can be achieved.Keywords: Work loyalty, Work stress, Supervisor support, Work productivity
Clustering Analysis Of Productive Age Unemployment Rates Using The K-Means Algorithm In Bekasi City Ningsih, Rahayu; Suharsono, Dhiya Firyal; Muryani, Sri; Rukiastiandari, Sinta; Ferliyanti, Herlina
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Unemployment among the productive-age population remains a significant issue in urban areas, particularly in Bekasi City, which recorded an open unemployment rate of 7.82% in August 2024—much higher than the national average of 4.91%. This study aims to classify productive-age unemployment using a machine learning approach with the K-Means Clustering algorithm to provide a more comprehensive understanding of unemployment patterns. This research adopts a quantitative approach with the use of SPSS version 25 and RapidMiner software. SPSS is used for validity, reliability, multicollinearity tests, optimal cluster determination, and ANOVA. Primary data were collected through a questionnaire distributed to 100 respondents, with 75 valid responses meeting the criteria (productive age, unemployed, and actively seeking employment). The research variables include age, education level, unemployment duration, perception of job opportunities, self-perception, and factors causing unemployment. The K-Means analysis resulted in three main clusters: the pessimistic cluster (low motivation), the neutral cluster (moderate perception), and the optimistic cluster (high motivation). Evaluation using ANOVA showed that the variables significantly differentiate between the clusters. These findings emphasize that productive-age unemployment is heterogeneous and requires cluster-based specific policies.
Co-Authors Ananto Krisna Wardhana Andrie Kurniawan Ani Yoraeni Aprillia Aprillia, Aprillia Bayu Jayus Suwito Candra, Edo Nado Chodidjah Chodidjah Dede Suleman Devy Sofyanty Edo Nado Candra Eneng Iviq Hairo Rahayu Faif Yusuf Fara Mutia Faroman Syarief Hairo Rahayu, Eneng Iviq Hamdun Sulaiman Herlina Ferliyanti Ida Zuniarti, Ida Isyana Emita Joko Ariawan Juarni Siregar Juarni Siregar Juarni Siregar Karlina, Eulin Khopidoh Sayekti Kristina, Ayang Lilik Yuliawati M.Suwandi, M.Suwandi Maemunah Maemunah Meilania Linasari Mira Retaniama Muhammad Suwandi Mutia, Fara Rachmat Fadly Raden Mohamad Herdian Bhakti Rahayu Ningsih Rahayu Ningsih Riris Lestiowati Rohimah, Luthfia Sapto Widiyanto Silvy Amelia - Universitas BSI Siregar, Juarni Sri Muryani, Sri Sri Rusiyati Suhardoyo Suharsono, Dhiya Firyal Suwandi, Muhammad Suwito, Bayu Jayus Taufik , Ahmad Teo Filus Simanjuntak Widiarina . Yohanes Ulu Mado Yunita Yunita Zahra Zahra