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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Innovation Information Technology and Application (JINITA) Jurnal Media Informatika Jurnal Sains Teknologi dan Sistem Informasi Komatika: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Ekonomi, Pendidikan dan Teknik (IDENTIK) Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA) Jurnal Nasional Komputasi dan Teknologi Informasi
Ersha Aisyah Elfaiz
Universitas Negeri Surabaya
Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Neuroscience Public Health Social Sciences Transportation

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The Barriers of Knowledge Acquisition and Knowledge Transfer in Software Companies: A Systematic Literature Review Ersha Aisyah Elfaiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2522

Abstract

Nowadays, organizations need computer-based information systems to improve their business process. Software company provide service to accommodate that needs. In the development process, knowledge acquisition and knowledge transfer are the most important process to understand the technology used. However, knowledge gap is a problem that may occur. To solve that, this research provides the barriers or problems that may occur in two process and the solutions. From the systematic literature review, we get 20 studies that relevant. Then, we know that knowledge acquisition barriers caused by human and organization. Knowledge transfer barriers caused by human, organization and technology. We also summarize the solution that proposed in the studies.
Peningkatan Literasi Digital dan Keamanan Berbasis Etika pada Guru: Studi Kasus Sosialisasi Etika dan Keamanan Media Sosial di SMA Negeri 1 Batu Muhammad Sonhaji Akbar; Rizky Basatha; Bambang Sujatmiko; Riza Akhsani Setyo Prayoga; Ersha Aisyah Elfaiz
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 2 (2025): November 2025
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v5i2.1370

Abstract

Kegiatan pengabdian masyarakat ini bertujuan meningkatkan literasi digital dan praktik keamanan bermedia sosial yang berorientasi etika bagi guru di SMA Negeri 1 Batu. Kegiatan dilaksanakan secara tatap muka pada 16 Juni 2025 dengan partisipasi sekitar 40 guru. Pendekatan yang digunakan berupa sosialisasi dan pelatihan aplikatif (workshop) berdurasi ±2,5 jam yang mengintegrasikan paparan konseptual, demonstrasi teknis (manajemen kata sandi, pengaturan privasi, verifikasi dua langkah, serta identifikasi serangan phishing), dan diskusi studi kasus. Evaluasi dilaksanakan melalui instrumen pre-test dan post-test yang dilengkapi kuesioner umpan balik untuk mengukur perubahan pengetahuan dan kesiapan penerapan. Hasil evaluasi menunjukkan adanya peningkatan pemahaman konseptual serta kesiapan praktis peserta dalam menerapkan langkah-langkah keamanan digital dasar, disertai respons positif terhadap relevansi dan aplikabilitas materi. Peserta mengusulkan perluasan cakupan materi misalnya aspek tindak pidana siber dan pemanfaatan kecerdasan buatan dalam konteks pendidikan serta rekomendasi untuk mekanisme pendampingan berkala. Kesimpulannya, intervensi ini efektif dalam meningkatkan kesadaran etika dan kapasitas dasar keamanan digital di kalangan guru; untuk memastikan keberlanjutan dan penguatan kompetensi, disarankan pengembangan modul lanjutan, monitoring berkala, dan evaluasi kuantitatif yang lebih komprehensif.
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

Publisher :

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;
Analisis Sentimen Performansi Operator Telekomunikasi di Indonesia Menggunakan Metode Text Mining Ersha Aisyah Elfaiz; Riza Akhsani Setyo Prayoga; Monica Cinthya; Muhammad Sonhaji Akbar; Rizky Basatha
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 1 (2025): April 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i1.4024

Abstract

The telecommunications sector in Indonesia has experienced rapid development in recent years, characterized by the increasing number of telecommunications operators offering various services and products. Therefore, there is competitive rivalry among operators. The right strategy is needed to survive and compete effectively. One of the efforts that can be made by telecommunication companies is to evaluate operational performance. This research aims to analyze the sentiment of X users towards telecommunication operational performance, at Telkomsel and Tri operators using text mining methods, namely Naïve Bayes, Support Vector Machine (SVM) and Decision Tree Learning (DTL). The research data is obtained by crawling from the X application, then the data is processed to remove unnecessary words or affixes. Then data modeling and validation is carried out using split validation and cross validation techniques. In the split validation technique, the data is divided into 70% training data and 30% testing data, while in the cross-validation technique the fold parameter is set to determine which fold has the highest accuracy. The results of the study show that the SVM method has the highest accuracy, where in split validation the accuracy is 84.29% for Telkomsel data and 75.70% for Tri data. Similarly, in cross validation, the accuracy is 82.15% on fold 4, 7 for Telkomsel data and 61.41% on fold 9 for Tri data. In addition, it is known that Telkomsel data has 18.64% positive sentiment and 81.36% negative sentiment. While Tri data has 61.11% positive sentiment and 38.89% negative sentiment.
Pengembangan Dan Uji Kelayakan Learning Management System Aksa Berorientasi Problem-Based Learning Ageng Ayu Illadana; Ersha Aisyah Elfaiz
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 2 (2026): JITIFNA - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i2.1893

Abstract

The widespread adoption of Learning Management Systems (LMS) in learning continues to increase, but their implementation has not been widely followed by the integration of learning models such as Problem-based Learning (PBL). This research aims to develop and test the feasibility of Aksa LMS, a Moodle-based LMS designed following the PBL syntax, which was tested based on four characteristics of software quality with ISO/IEC 25010 standards, namely functional suitability, interaction capability, performance efficiency, and reliability. This research is a Research and Development study with a quantitative approach using the ADDIE Analysis, Design, Development, Implementation, Evaluation method. A total of 97 students of the Department of Informatics Engineering, State University of Surabaya, who were determined through the Slovin formula, were involved as evaluators. Data were collected using a Guttman-scale questionnaire that was confirmed with black box testing, an adapted USE Questionnaire, load time measurement with GTMetrix, and continuous uptime monitoring. The results of the study showed that functional suitability reached 100%, black box testing was 98.4%, interaction capability was assessed positively with Wilcoxon Signed-Rank Test results p < 0.001 (< 0.05), the four pages tested on performance efficiency loaded below the 10-second limit, and reliability recorded an uptime ratio of 100%. These findings show that Aksa's LMS meets the eligibility criteria for all four characteristics and is suitable for use as a PBL-oriented learning medium.
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
Implementasi Algoritma Vector Space Model pada Website EmoAnalyzer untuk Klasifikasi Emosi Mahasiswa Rendi Eko Kurniawan; Lia Dwi Rusanti; Jihan Salsabilah; Siti Aulia Rahmadhani; Ersha Aisyah Elfaiz

Publisher :

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10265

Abstract

Abstrak - Perkembangan teknologi informasi memungkinkan mahasiswa mengekspresikan perasaan dan pengalaman akademik mereka melalui teks, salah satunya terkait pengerjaan tugas akhir atau skripsi. Namun, analisis emosi mahasiswa secara manual terhadap data teks dalam jumlah besar membutuhkan waktu dan berpotensi menimbulkan subjektivitas. Oleh karena itu, penelitian ini bertujuan untuk mengimplementasikan algoritma Vector Space Model (VSM) pada website EmoAnalyzer untuk mengklasifikasikan emosi mahasiswa berdasarkan komentar teks. Data yang digunakan berjumlah 150 komentar mahasiswa Universitas Negeri Surabaya yang diperoleh melalui kuesioner dan diklasifikasikan ke dalam tiga kategori emosi, yaitu senang, sedih, dan marah. Tahapan penelitian meliputi pengumpulan data, pelabelan data menggunakan kamus Lexicon bahasa Indonesia, text preprocessing, pembobotan TF-IDF, serta perhitungan cosine similarity menggunakan VSM. Hasil penelitian menunjukkan bahwa website EmoAnalyzer mampu mengklasifikasikan emosi mahasiswa dan menyajikan hasil analisis dalam bentuk persentase dan visualisasi grafik. Implementasi algoritma VSM pada EmoAnalyzer dapat menjadi solusi untuk membantu memantau kondisi emosional mahasiswa selama proses pengerjaan tugas akhir.Kata kunci: Vector Space Model; Website; Text Mining; Emosi; Mahasiswa; Abstract - The development of information technology allows students to express their feelings and academic experiences through texts, one of which is related to working on their final project or thesis. However, manual analysis of students' emotions on large amounts of text data takes time and has the potential to cause subjectivity. Therefore, this study aims to implement the Vector Space Model (VSM) algorithm on the EmoAnalyzer website to classify students' emotions based on text comments. The data used amounted to 150 comments of State University of Surabaya students obtained through questionnaires and classified into three categories of emotions, namely happy, sad, and angry. The research stages include data collection, data labeling using the Indonesian Lexicon dictionary, text preprocessing, TF-IDF weighting, and cosine similarity calculation using VSM. The results of the study show that the EmoAnalyzer website is able to classify students' emotions and present the results of the analysis in the form of percentages and graph visualizations. The implementation of the VSM algorithm on EmoAnalyzer can be a solution to help monitor students' emotional state during the process of working on the final project.Keywords: Vector Space Model; Website; Text Mining; Emotion; Student;
Pengembangan Lms Moodle Berbasis Project Based Learning Untuk Meningkatkan Kompetensi Kognitif Pada Elemen Pemrograman Web Siswa Kelas Xi Rpl Smk Negeri 4 Bojonegoro Salvia Nabillah Syifa; Ersha Aisyah Elfaiz
Jurnal Ilmu Ekonomi, Pendidikan dan Teknik Vol. 3 No. 4 (2026): IDENTIK - Juli
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/identik.v3i4.1734

Abstract

The implementation of digital learning media for the Web Programming element at SMK Negeri 4 Bojonegoro remains significantly constrained, leading to a high reliance on conventional teaching methods and a lack of a specialized Learning Management System (LMS) that supports project-based learning. This research aims to develop a Project-Based Learning (PjBL)-based Moodle LMS called DigiLearn and analyze its effectiveness in improving the cognitive competence of students. Using a Research and Development (R&D) approach with the ADDIE model, this study employed a Nonequivalent Control Group Design. The study involved two classes of eleventh-grade Software Engineering (RPL) students, split into an experimental group (XI RPL 1) and a control group (XI RPL 2), each consisting of 34 students. The developed LMS was integrated with the Monitoring Kanban Board plugin to help students track their project milestones through To Do, Doing, and Done categories. Data collection was conducted via expert validation and pretest-posttest instruments, subsequently analyzed using feasibility percentages, N-Gain tests, and Independent Samples t-Tests. The validation results demonstrated that the LMS is highly feasible for implementation. The N-Gain analysis revealed that the experimental group achieved a score of 0.712 (high category), significantly outperforming the control group's score of 0.381 (moderate category). Furthermore, the Independent Samples t-Test confirmed a statistically significant difference in cognitive competence between the two groups. It is concluded that the PjBL-based Moodle LMS is highly effective in enhancing students' cognitive competence within the Web Programming element.
IMPLEMENTASI ARTIFICIAL INTELLIGENCE DALAM PEMBELAJARAN: OPTIMALISASI PENILAIAN BERBASIS HOTS DI SMKN 10 SURABAYA Muhammad Sonhaji Akbar; Ramadhan Cakra Wibawa; Ersha Aisyah Elfaiz; I Gusti Lanang Putra Eka Prismana; Atan Pramana; Raka Tegar Wicaksono
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 7 No. 4 (2026): Vol. 7 No. 4 (2026)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v7i4.62375

Abstract

Revolusi Generative Artificial Intelligence (GenAI) menuntut pendidik vokasi mentransformasi evaluasi hasil belajar dari tingkat rendah ke instrumen berorientasi Higher Order Thinking Skills (HOTS). Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan kompetensi digital guru SMKN 10 Surabaya dalam merancang instrumen penilaian HOTS yang selaras dengan Kurikulum Merdeka menggunakan platform Kinantiku.com. Mitra menghadapi tantangan tingginya beban administratif harian dan keterbatasan waktu merekayasa butir soal tingkat menganalisis (C4), mengevaluasi (C5), dan mencipta (C6). Pelaksanaan PkM mencakup lima tahapan: analisis kebutuhan diagnostik, penyusunan modul prompt engineering, workshop intensif, pendampingan MBKM, dan evaluasi dampak. Hasil evaluasi terhadap 70 responden guru menunjukkan 95,7% peserta mengonfirmasi bahwa integrasi AI sangat membantu efisiensi waktu penyusunan Tes Kemampuan Akademik (TKA) dan bank soal HOTS. Selain itu, 98,6% responden memberikan penilaian tinggi terhadap kemudahan operasional platform. Program ini berkontribusi pada pencapaian Sustainable Development Goals (SDGs), khususnya SDG 4 (xPendidikan Berkualitas), SDG 5 (Kesetaraan Gender), SDG 8 (Pekerjaan Layak dan Pertumbuhan Ekonomi), dan SDG 17 (Kemitraan untuk Mencapai Tujuan). Kemitraan terstruktur ini terbukti membangun ekosistem evaluasi pembelajaran yang adaptif dan efisien.
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 Ageng Ayu Illadana Bambang Sujatmiko I Gusti Lanang Putra Eka Prismana Jihan Salsabilah Lia Dwi Rusanti M. Hamdan Fuadi Moch. Anang Ardiansyah Monica Cinthya Muhammad Hamdan Fuadi Muhammad Sonhaji Akbar Pramana, Atan Raka Tegar Wicaksono Ramadhan Cakra Wibawa Rendi Eko Kurniawan Rino Raihan Gumilang Riza Akhsani Setyo Prayoga Rizky Basatha Salvia Nabillah Syifa Siti Aulia Rahmadhani Solikhul Mauludin