Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Tekno Kompak Jurnal Informatika dan Rekayasa Perangkat Lunak
Miechael, Miechael
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis Perancangan Sistem Informasi Pelayanan Perbaikan Hardware Komputer Berbasis Website Menggunakan Metode Backward Chaining Miechael, Miechael; Daniawan, Benny
Jurnal Tekno Kompak Vol 18, No 1 (2024): FEBRUARI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v18i1.3441

Abstract

Perkembangan sistem informasi (SI) dan teknologi informasi (TI) sangat pesat. Kemajuan SI dan TI berperan penting terhadap kinerja suatu organisasi atau perusahaan menjadi lebih efektif dan efisien. Kegiatan yang sebelumnya mengharuskan bertatap muka, saat ini dituntut agar dapat dilakukan tanpa harus bertemu secara langsung. Layanan berbasis online saat ini benyak bermunculan, layanan tersebut dapat menjangkau pelanggan secara langsung dan mempermudah setiap aktifitas yang dilakukan. Kecepatan dan kehandalan proses layanan menjadi hal utama yang diperhatikan penuh oleh organisasi atau perusahaan karena berpengaruh pada kepuasan pelanggan. Oleh karena itu untuk dapat meningkatkan kualitas layanan yang dimiliki CV. ONG COMPUTER khususnya dalam proses konsultasi, tracking progres pengerjaan, dan pemberian informasi mengenai status unit. Maka dibuat sebuah sistem usulan berbasis website yang menggunakan metode Backward Chaining agar sistem bisa mengenali kerusakan berdasarkan keluhan pelanggan, dan dapat membantu teknisi dalam proses pemberian informasi kepada pelanggan, serta tracking progres pengerjaan unit yang diperbaiki. Hasil dari pengujian sistem menggunakan metode Technology Acceptance Model (TAM) terhadap 76 responden dengan nilai standar t-table sebesar 1,994 menunjukan hasil bahwa variabel Perceived Usefulness (PU) tidak berpengaruh terhadap Attitude Toward Using (ATU) sebesar 1,443, PU berpengaruh terhadap Behavioral Intention to Use (BITU) sebesar 4,650, Perceived Ease of Use (PEOU) berpengaruh terhadap ATU sebesar 2,829, ATU bepengaruh terhadap BITU sebesar 6,785, sedangkan BITU berpengaruh terhadap Actual System Use (ASU) sebesar 15,326.Kata Kunci: Sistem Informasi Perbaikan, Sistem Rekomendasi Perbaikan Hardware Komputer, Backward Chaining, Technology Acceptance Model, Behavior Intention to Use
Comparison of Individual Algorithms (Decision Tree, Naïve Bayes, and Support Vector Machine) and Ensemble Voting in Predicting Students’ On-Time Graduation Based on Course Grades Ferdian, Sevtian; Miechael, Miechael; Wibowo, Arief
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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

Abstract

Education plays an important role in improving the quality of human resources and supporting a country’s progress toward becoming a developed nation. Higher education institutions serve as one of the providers of formal education, where the quality of these institutions is measured through accreditation. One of the key indicators influencing accreditation is the outcomes and achievements of the Tri Dharma of higher education, which include the timeliness of student graduation. This study aims to compare models for predicting on-time student graduation using three machine learning algorithms, namely Decision Tree, Naïve Bayes, and Support Vector Machine (SVM), as well as their combination through the Ensemble Voting method. The prediction is based on historical grade data from courses taken during semesters one to four. The research methodology adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM), which consists of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset used in this study consists of 2,471 records with 11 attributes. Data preprocessing was conducted through data cleaning and class balancing using under sampling techniques. The results indicate that the Ensemble Voting model using the Soft Voting method achieves the best performance, with an accuracy of 91.80%, precision of 91.87%, and recall of 91.80%, outperforming the individual models of Decision Tree, Naïve Bayes, and SVM. The implementation of this model can be utilized to predict students’ on-time graduation based on course grade inputs. Therefore, this research can serve as a supporting tool for early detection of potential delays in student graduation.
Co-Authors Arief Wibowo Daniawan, Benny Ferdian, Sevtian