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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Media Informatika Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK) Jurnal Nasional Komputasi dan Teknologi Informasi
Solikhul Mauludin
Universitas Negeri Surabaya
Religion Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Klasifikasi Abstrak Tugas Akhir Mahasiswa Berbasis Web dengan Algoritma K-Nearest Neighbor Moch. Anang Ardiansyah; Raka Tegar Wicaksono; Rino Raihan Gumilang; Solikhul Mauludin; Muhammad Hamdan Fuadi; Ersha Aisyah Elfaiz

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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10213

Abstract

Abstrak - Penelitian ini mengembangkan sistem klasifikasi abstrak tugas akhir mahasiswa Program Studi Pendidikan Teknologi Informasi (PTI) berbasis web menggunakan algoritma K-Nearest Neighbor (KNN). Data dikumpulkan melalui web scraping dari jurnal IT-Edu pada rentang tahun 2017–2025, kemudian melalui tahap preprocessing meliputi case folding, tokenizing, stopword removal, dan stemming sebelum dilakukan ekstraksi fitur menggunakan TF-IDF dan pengukuran kemiripan dengan cosine similarity. Dataset berjumlah 312 abstrak dibagi menjadi 249 data latih dan 63 data uji. Evaluasi performa dilakukan menggunakan beberapa nilai K dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa nilai K= 17 memberikan kinerja terbaik dengan F-Measures sebesar 0.539. Sistem berbasis web yang dihasilkan mampu melakukan klasifikasi otomatis abstrak tugas akhir ke dalam kategori Rekayasa Perangkat Lunak (RPL) dan Teknik Komputer dan Jaringan (TKJ), sehingga dapat mendukung pengelolaan repositori akademik secara lebih efisien dan objektif.Kata kunci: K-Nearest Neighbor; TF-IDF; Cosine Similarity; Klasifikasi Dokumen; Sistem Berbasis Web; Abstract - This study develops a web-based classification system for undergraduate thesis abstracts in the Information Technology Education (PTI) program using the K-Nearest Neighbor (KNN) algorithm. Data were collected through web scraping from the IT-Edu journal (2017–2025) and preprocessed through case folding, tokenizing, stopword removal, and stemming prior to feature extraction using TF-IDF and similarity measurement with cosine similarity. A total of 312 abstracts were obtained and divided into 249 training data and 63 testing data. System performance was evaluated using several K values and measured with accuracy, precision, recall, and F1-score metrics. The results indicate that K = 17 provides the best performance with an F-measure of 0.539. The developed web-based system can automatically classify thesis abstracts into Software Engineering (RPL) and Computer and Network Engineering (TKJ), supporting more efficient and objective management of academic repositories.Keywords: K-Nearest Neighbor; TF-IDF; Cosine Similarity; Text Classification; Web-based System;
Perencanaan Manajemen Proyek Dalam Meningkatkan Efisiensi dan Efektivitas Penjadwalan Mata Kuliah Raka Tegar Wicaksono; M. Hamdan Fuadi; Moch. Anang Ardiansyah; Solikhul Mauludin; Rino Raihan Gumilang; Ersha Aisyah Elfaiz
Jurnal Media Informatika Vol. 7 No. 1 (2026): Edisi Januari - Februari
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v7i1.7967

Abstract

Perkembangan teknologi informasi mendorong perguruan tinggi untuk mengoptimalkan proses akademik, termasuk penjadwalan mata kuliah yang sering mengalami konflik jadwal serta kesalahan input. Penelitian ini bertujuan untuk menyusun perencanaan manajemen proyek pengembangan aplikasi penjadwalan berbasis web bernama Jadwalin guna meningkatkan efisiensi dan efektivitas proses penjadwalan akademik. Penelitian menggunakan metode deskriptif dengan pendekatan studi kasus serta mengacu pada kerangka Project Management Body of Knowledge (PMBOK). Perencanaan dilakukan melalui penyusunan Work Breakdown Structure (WBS), manajemen waktu menggunakan Gantt Chart, dan estimasi biaya dengan pendekatan bottom-up. Hasil perencanaan menunjukkan bahwa penerapan sembilan area pengetahuan manajemen proyek memungkinkan proses pengembangan berjalan lebih terarah melalui integrasi ruang lingkup, jadwal, biaya, kualitas, komunikasi, risiko, sumber daya, dan pengadaan. Perencanaan yang matang juga membantu meminimalkan risiko keterlambatan, menghindari scope creep, serta memastikan alur pengembangan sesuai kebutuhan akademik. Dengan demikian, aplikasi Jadwalin diharapkan mampu mendukung digitalisasi kampus, meminimalkan bentrokan jadwal, dan meningkatkan pengalaman pengguna dalam pengelolaan jadwal kuliah
Efektivitas Media Pembelajaran Cling (Clean Learning Integrated Gateway) Terhadap Peningkatan Keterampilan Clean Code Siswa Smk Solikhul Mauludin; Elvira Wardah
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 5 (2026): IDENTIK - September
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i5.1898

Abstract

Clean, well-structured code is one competency demanded by Indonesia's National Work Competency Standard for vocational programming graduates, yet coding classes commonly still focus only on whether a program runs, without giving automatic feedback on code quality. This study examines the effectiveness of CLING (Clean Learning Integrated Gateway), a website-based Python compiler integrated with Pylint for automatic clean code assessment. The method used was Research and Development with the ADDIE model, combined with a Quasi-Experimental Nonequivalent Control Group Design involving tenth-grade Software Engineering students at SMK Antartika 2 Sidoarjo: an experimental class (25 students) taught with CLING and a control class (25 students) taught with Google Colab without linter integration. Material and media expert validation scored 88.2% and 87.7% (Very Valid). The Shapiro-Wilk test showed the control class posttest data were not normally distributed (p = 0.002), so the Mann-Whitney U Test was used, yielding U = 604.0 (p < 0.001), indicating a significant difference in clean code skills between the two classes. The experimental class posttest average (65.77) was far higher than the control class (32.51), with an N-Gain of 0.488 (moderate category). The Technology Acceptance Model questionnaire recorded a practicality score of 87.55% (Very Practical). These results indicate that CLING effectively improves vocational students' clean code skills.    
Klasifikasi Abstrak Tugas Akhir Mahasiswa Berbasis Web dengan Algoritma K-Nearest Neighbor Moch. Anang Ardiansyah; Raka Tegar Wicaksono; Rino Raihan Gumilang; Solikhul Mauludin; Muhammad Hamdan Fuadi; Ersha Aisyah Elfaiz
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 1 (2026): Februari, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.216

Abstract

Abstrak - Penelitian ini mengembangkan sistem klasifikasi abstrak tugas akhir mahasiswa Program Studi Pendidikan Teknologi Informasi (PTI) berbasis web menggunakan algoritma K-Nearest Neighbor (KNN). Data dikumpulkan melalui web scraping dari jurnal IT-Edu pada rentang tahun 2017–2025, kemudian melalui tahap preprocessing meliputi case folding, tokenizing, stopword removal, dan stemming sebelum dilakukan ekstraksi fitur menggunakan TF-IDF dan pengukuran kemiripan dengan cosine similarity. Dataset berjumlah 312 abstrak dibagi menjadi 249 data latih dan 63 data uji. Evaluasi performa dilakukan menggunakan beberapa nilai K dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa nilai K = 17 memberikan kinerja terbaik dengan F-Measures sebesar 0.539. Sistem berbasis web yang dihasilkan mampu melakukan klasifikasi otomatis abstrak tugas akhir ke dalam kategori Rekayasa Perangkat Lunak (RPL) dan Teknik Komputer dan Jaringan (TKJ), sehingga dapat mendukung pengelolaan repositori akademik secara lebih efisien dan objektif. Kata kunci: K-Nearest Neighbor; TF-IDF; Cosine Similarity; Klasifikasi Dokumen; Sistem Berbasis Web; Abstract - This study develops a web-based classification system for undergraduate thesis abstracts in the Information Technology Education (PTI) program using the K-Nearest Neighbor (KNN) algorithm. Data were collected through web scraping from the IT-Edu journal (2017–2025) and preprocessed through case folding, tokenizing, stopword removal, and stemming prior to feature extraction using TF-IDF and similarity measurement with cosine similarity. A total of 312 abstracts were obtained and divided into 249 training data and 63 testing data. System performance was evaluated using several K values and measured with accuracy, precision, recall, and F1-score metrics. The results indicate that K = 17 provides the best performance with an F-measure of 0.539. The developed web-based system can automatically classify thesis abstracts into Software Engineering (RPL) and Computer and Network Engineering (TKJ), supporting more efficient and objective management of academic repositories. Keywords: K-Nearest Neighbor; TF-IDF; Cosine Similarity; Text Classification; Web-based System;
Co-Authors Elvira Wardah Ersha Aisyah Elfaiz M. Hamdan Fuadi Moch. Anang Ardiansyah Muhammad Hamdan Fuadi Raka Tegar Wicaksono Rino Raihan Gumilang