Article Per Year (5 Year)
p-Index From 2021 - 2026
7.523
P-Index
This Author published in this journals
All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Informatika dan Teknik Elektro Terapan Jurnal IPTEK Jurnal Sistem Informasi dan Bisnis Cerdas JURNAL INSTEK (Informatika Sains dan Teknologi) Prosiding Seminar Nasional Sains dan Teknologi Terapan JURNAL TEKNOLOGI DAN OPEN SOURCE Data Science: Journal of Computing and Applied Informatics Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal IJEEIT : International Journal of Electrical Engineering and Information Technology Applied Technology and Computing Science Journal Journal of Information Systems and Informatics Abdimas Universal bit-Tech Jurnal Teknologi Dan Sistem Informasi Bisnis Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Advances in Data and Information Systems Jifosi Nusantara Science and Technology Proceedings KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Penelitian Inovatif Konstelasi: Konvergensi Teknologi dan Sistem Informasi PELS (Procedia of Engineering and Life Science) Journal of Artificial Intelligence and Engineering Applications (JAIEA) NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Mesin, Industri, Elektro dan Informatika Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Jurnal Ilmiah Teknik Informatika dan Komunikasi Madani: Multidisciplinary Scientific Journal Scientica: Jurnal Ilmiah Sains dan Teknologi Student Research Journal Jurnal Informatika Polinema (JIP) Jurnal Ilmu Komputer dan Sistem Informasi Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal ilmiah teknologi informasi Asia Jurnal Pengabdian Masyarakat Multidisiplin
Amalia Anjani Arifiyanti
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 70 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 70 Documents
Search

 3   4   5   6   7 
KLASTERISASI TRACER STUDY ALUMNI UNIVERSITAS XYZ MENGGUNAKAN ALGORITMA K-MEANS Fernaldy, Fabiyan Atha; Arifiyanti, Amalia Anjani; Kartika, Dhian Satria Yudha
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5581

Abstract

Penelitian ini bertujuan untuk menganalisis dan mengelompokkan data alumni berdasarkan Indeks Prestasi Kumulatif (IPK) dan masa tunggu untuk mendapatkan pekerjaan menggunakan algoritma K-Means. Metode Elbow dan Silhouette Score diterapkan untuk menentukan jumlah cluster yang optimal. Hasil evaluasi menunjukkan bahwa untuk dataset yang dianalisis, jumlah cluster optimal untuk dataset pertama adalah tiga, sedangkan untuk dataset kedua adalah dua, dengan nilai Silhouette Score tertinggi masing-masing 0.497656 dan 0.502767. Deskripsi hasil clustering mengungkapkan perbedaan karakteristik antara cluster, di mana cluster dengan rata-rata IPK tertinggi memiliki masa tunggu terendah untuk mendapatkan pekerjaan. Temuan ini memberikan wawasan berharga bagi pengembangan kurikulum dan program bimbingan karir, serta meningkatkan pemahaman tentang pola karir alumni. Penelitian ini diharapkan dapat menjadi referensi untuk studi lebih lanjut dalam bidang analisis data dan pengembangan pendidikan.
ASPECT-BASED SENTIMENT ANALYSIS PADA ULASAN APLIKASI ACCESS BY KAI MENGGUNAKAN METODE TF-IDF DAN ALGORITMA SUPPORT VECTOR MACHINE Nur Rachman Nidhi Suryono, Muhammad; Amalia Anjani Arifiyanti; Dhian Satria Yudha Kartika
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 2 (2025): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i2.57155

Abstract

Access by KAI merupakan aplikasi layanan transportasi digital dari PT Kereta Api Indonesia yang mempermudah pengguna dalam mengakses layanan perjalanan kereta api. Untuk meningkatkan kualitas layanan dan pengalaman pengguna, penelitian ini melakukan analisis sentimen terhadap ulasan pengguna aplikasi menggunakan algoritma Support Vector Machine (SVM). Tiga aspek utama yang dianalisis yaitu Financial Transactions, Technical Issues and Performance, serta User Experience and Interface. Penelitian menggunakan kombinasi metode sampling (SMOTE dan Non-SMOTE), kernel (Linear, RBF, Polynomial), dan pembagian data (80:20 dan 70:30) untuk menemukan model terbaik. Hasil terbaik untuk aspek Financial Transactions diperoleh dari model SMOTE dengan kernel RBF dan rasio 70:30 (akurasi 0.9270). Untuk Technical Issues and Performance, model terbaik adalah Non-SMOTE dengan kernel Linear dan rasio 70:30 (akurasi 0.8718). Sedangkan untuk User Experience and Interface, model Non-SMOTE dengan kernel Linear dan rasio 80:20 memberikan akurasi tertinggi sebesar 0.8825. Model terbaik ini diimplementasikan dalam aplikasi web berbasis Flask yang dapat memprediksi sentimen, mengekspor hasil dalam bentuk .csv, serta menampilkan visualisasi data. Hasil implementasi menunjukkan bahwa kombinasi model terpilih mampu memberikan pemetaan sentimen yang konsisten dan terstruktur terhadap ulasan pengguna, sehingga dapat digunakan sebagai dasar evaluasi berbasis data dalam pengembangan fitur aplikasi.
Application of Ensemble Machine Learning Methods for Aspect-Based Sentiment Analysis on User Reviews of the Wondr by BNI App Hardiartama, Rendi; Arifiyanti, Amalia Anjani; Ana Wati3, Seftin Fitri
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4297

Abstract

This study analyzes user perceptions of the Wondr by BNI app using Aspect-Based Sentiment Analysis (ABSA) and a stacking ensemble learning approach on user reviews. Data were collected from the Google Play Store and App Store through scraping, then processed and labeled. The study involves two classification stages: aspect identification and sentiment classification for each aspect. The stacking ensemble model without resampling showed the best performance, with F1-scores of 99.4% for UI (User Interface), 99.3% for Authentication, and 99% for Transaction. For sentiment classification, F1-scores reached 82.2% User Interface (UI), 87.8% (Authentication), and 92.4% (Transaction). The use of LIME (Local Interpretable Model-Agnostic Explanations) improved model interpretability by highlighting keywords influencing the classification results. The final output of this research is a website capable of performing aspect-based sentiment classification
Classification and Mapping of Online Gambling Based on News Articles Using NER and SVM Wisnu Mukti Darwansah; Amalia Anjani Arifiyanti; Rizka Hadiwiyanti
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.4707

Abstract

The phenomenon of online gambling in Indonesia has developed rapidly, posing serious social and economic threats. This thesis aims to classify and map online gambling activities based on digital news using the Support Vector Machine (SVM) algorithm and Named Entity Recognition (NER). Data were collected from the news portals Detik.com, Kompas.com, and Tribunnews from 2017 to 2024 through a web scraping approach. The research process included setup and library import, data upload, data exploration, data labeling according to Law No. 1 of 2023, data preprocessing, data filtering, location normalization and extraction, and location data cleaning. Subsequently, the SVM model was trained for risk classification and followed by prediction. Evaluation was conducted using accuracy and F1-score metrics to assess overall model performance and classification balance. Based on the evaluation results, the Normal SVM model demonstrated the best performance with an accuracy of 96.94% and an F1-score of 0.97. The findings indicate that the combination of NER and SVM effectively identifies the location and risk level of online gambling activities. This research is expected to contribute to law enforcement authorities and policymakers in their efforts to prevent and address online gambling activities in Indonesia.
Implementation of an Executive Information System for Thesis Document Submission with the Addition of AES-256-CBC Cryptography Algorithm Aufa, Taqiyuddin Ahmad Al; Rivaldy, Adriano Femaz; Arifiyanti, Amalia Anjani; Putra, Agung Brastama
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1803

Abstract

The rapid digitalization of higher education demands secure and efficient management of academic documents such as thesis submissions. This study aims to develop an Executive Information System (EIS) for Thesis Document Submission integrated with AES-256-CBC cryptographic security to ensure data confidentiality, integrity, and controlled access. The system is implemented as a web-based platform using the Laravel framework and MySQL database, where each uploaded thesis document is automatically encrypted, and only authorized users with a valid Master Key can decrypt it. The AES-256-CBC algorithm generates unique ciphertexts for every encryption process, supported by randomized Initialization Vectors and separate key management to prevent unauthorized access or data leakage. Furthermore, the EIS dashboard implements the drill-down method, presenting real-time analytical information. This allows academic leaders to navigate hierarchically from high-level summaries to specific, detailed data, enhancing their ability to monitor thesis submissions and make informed decisions effectively. The results indicate that the integration of cryptography and executive information management enhances both document security and administrative efficiency, providing a reliable and transparent solution for safeguarding academic data within higher education institutions.
Convolutional Neural Network Approach for Aspect-Based Sentiment Analysis of Tourism Reviews Siti Oktavia Eka Putri; Amalia Anjani Arifiyanti; Abdul Rezha Efrat Najaf
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2582

Abstract

The tourism industry is a key economic sector in Indonesia, with East Java ranking highest in tourist visits. This study aims to enhance tourism development by applying aspect-based sentiment analysis (ABSA) using convolutional neural networks (CNN) to analyze online reviews. CNN was selected for this study due to its proven efficiency in capturing local n-gram features and its relatively lower computational cost compared to other deep learning model. Reviews from TripAdvisor and Google Maps were collected focusing on four aspects: attraction, amenities, access, and price. Five different models were developed in this research: one multilabel aspect classifier designed to identify multiple aspects mentioned within each review, and four sentiment classifiers focused on evaluating the sentiment polarity for each specific aspect. These models were trained and evaluated using various combinations of word embeddings, including static embeddings like Word2Vec, and contextualized embeddings such as BERT and IndoBERT. Additionally, the impact of preprocessing through stemming was investigated to understand how simplifying word forms affects model performance. Results indicate that IndoBERT-CNN attains the best overall sentiment classification, reaching F1-scores up to 0.71 for attraction and 0.93 for amenities, while Word2Vec-CNN with stemming leads multilabel classification. Meanwhile stemming improves performance for static embeddings like Word2Vec by simplifying word forms, it reduces effectiveness in transformer-based models like BERT and IndoBERT that rely on natural language context. These findings highlight the benefit of choosing appropriate embeddings and preprocessing for different tasks, thus providing practical insights for improving tourism services through better tourist reviews analysis.
Comparison of Adam, RMSprop, and SGD on DenseNet121 for Tomato Leaf Disease Classification Heni Lusiana Dewi; Amalia Anjani Arifiyanti; Abdul Rezha Efrat Najaf
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2684

Abstract

Diseases affecting tomato leaves can severely impact agricultural productivity, as they can reduce crop yields and quality significantly. A swift and dependable identification of these diseases is vital for ensuring prompt interventions and the successful implementation of disease control strategies. This study focus on evaluating and comparing the efficiency of three separate optimizers, such as Adam, RMSProp, and SGD on the pretrained Convolutional Neural Network (CNN) architecture DenseNet121. There has been no previous research that directly compares the performance of Adam, RMSProp, and SGD optimizers on the DenseNet121 model for classifying tomato leaf diseases using the Plant Village dataset. These optimizers are crucial in the training process by influencing the model’s ability to converge and generalize well on new, unseen data. Experimental procedures were performed using a labeled dataset of tomato leaf images, which included healthy leaves and various disease classes. Out of the three optimization techniques tested, the DenseNet121 model trained with the Adam optimizer consistently outperformed the others. It achieved the highest evaluation metrics, with an accuracy of 0.9800, precision of 0.9807, recall of 0.9800, and F1-score of 0.9800 on the test set. These outcomes suggest that the model has a strong and balanced classification performance, capable of correctly identifying disease conditions with minimal errors. Based on these findings, the DenseNet121 architecture combined with the Adam optimizer is considered the optimal model used to recognize various tomato leaf diseases in this study.
Detection of ARP Poisoning on Wireless LAN Using Machine Learning: Random Forest and AdaBoost Ersamazaya, Rafi Dhafin; Arifiyanti, Amalia Anjani; Kartika, Dhian Satria Yudha
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3364

Abstract

ARP poisoning is a prevalent security threat in Wireless Local Area Networks (WLANs), enabling attackers to manipulate ARP tables and perform man-in-the-middle attacks. This study develops a machine learning-based detection system to identify ARP poisoning incidents in real-time, using Random Forest, AdaBoost, and a hybrid Random Forest-AdaBoost ensemble model. Data was collected from a public Wi-Fi environment in Surabaya, consisting of 11,225 ARP traffic records, augmented with simulated ARP poisoning attacks. Data preprocessing included exploratory analysis, feature engineering, encoding, and dataset balancing to improve model performance. Experimental results demonstrate that the hybrid ensemble model achieved the highest accuracy (99.92% on validation and 99.94% on testing), but its inference time of 517.30 ms rendered it unsuitable for real-time deployment. In contrast, the AdaBoost model achieved similar accuracy with significantly faster inference latency (7.82–14.93 ms), making it the most efficient model for live monitoring. The optimized AdaBoost classifier was then deployed through a Telegram-based alert system integrated with Scapy for continuous packet inspection and immediate attack notifications. This study contributes to the advancement of real-time intrusion detection mechanisms for WLAN environments by demonstrating the effectiveness of ensemble learning in ARP poisoning detection. Furthermore, it emphasizes the importance of balancing detection accuracy with computational efficiency for practical deployment in dynamic network environments. The findings offer insights into developing scalable, low-latency security solutions and lay the groundwork for future research on adaptive, real-time detection frameworks.
Pelatihan dan Pendampingan E-Learning Berbasis Gamifikasi Menggunakan Moodle untuk Meningkatkan Kompetensi Guru Rinjeni, Tri Puspa; Safitri, Eristya Maya; Arifiyanti, Amalia Anjani
JUPAMU: Jurnal Pengabdian Masyarakat Multidisiplin Vol. 1 No. 3: Mei 2026 (Dalam Proses)
Publisher : Ihsan Cahaya Pustaka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66031/jupamu.v1i3.296

Abstract

Rendahnya keterlibatan siswa dalam pembelajaran daring menjadi tantangan utama yang dihadapi oleh para pendidik di era digital. Pengabdian masyarakat ini bertujuan untuk meningkatkan kompetensi guru dalam merancang media pembelajaran yang interaktif melalui integrasi elemen gamifikasi pada platform e-learning Moodle. Metode pelaksanaan kegiatan dilakukan melalui enam tahapan sistematis, meliputi Focus Group Discussion (FGD), pelatihan teknis penggunaan Moodle, praktik perancangan gamifikasi, implementasi mandiri oleh guru, pendampingan berkelanjutan, hingga evaluasi kegiatan. Kegiatan ini melibatkan 31 guru sebagai peserta pendampingan. Hasil pengabdian menunjukkan bahwa integrasi fitur gamifikasi seperti sistem poin, level, leaderboard, dan badges efektif dalam mentransformasi e-learning menjadi media pembelajaran yang lebih menarik. Berdasarkan evaluasi kepuasan peserta, kegiatan ini memperoleh skor rata-rata sebesar 3,91 dari skala 4,00, dengan tingkat kepuasan tertinggi pada aspek kompetensi pemateri (3,94). Disimpulkan bahwa pelatihan ini berhasil meningkatkan kemampuan teknis dan pedagogis guru dalam memanfaatkan teknologi gamifikasi untuk menciptakan ekosistem pembelajaran digital yang partisipatif.
Analisis Komparatif Embedding Semantik Berbasis Large Language Model Pada Sistem Rekomendasi Buku Serendipitous di Perpustakaan Kampus Rahayu Kartika Sari; Eka Dyar Wahyuni; Amalia Anjani Arifiyanti
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.443

Abstract

The phenomenon of information overload in academic libraries often makes it difficult for users to discover relevant books, which may reduce reading interest. Conventional recommender systems are also prone to filter bubbles and tend to perform poorly under cold-start conditions. This study proposes a sequential recommendation system based on the Self-Attention Based Sequential Recommendation (SASRec) model integrated with five semantic embedding models, namely Word2Vec, BERT Multilingual, OpenAI text-embedding-3-small, Gemini-embedding-001, and Qwen3-Embedding-0.6B, to generate accurate and serendipitous recommendations. In addition, the Serendipity-Oriented Greedy (SOG) re-ranking algorithm is implemented to balance recommendation relevance and serendipity. The data set consists of 14,502 book records and 5,445 user interaction histories after the data cleaning process. Evaluation was conducted under three testing scenarios, namely the all-test set, warm test set, and cold test set, by comparing all model variants before and after the re-ranking process. The results show that the integration of Large Language Model (LLM)-based embeddings consistently improves performance compared to the standard SASRec model and traditional embeddings. Qwen3-Embedding-0.6B achieved the best performance, improving HitRate@10 by up to 282.9% and NDCG@10 by up to 387.8%, while maintaining semantic robustness in cold-start scenarios with an UnSerendipity@K score of 0.613. The implementation of SOG re-ranking reveals a direct trade-off between recommendation accuracy and diversity. Lightweight weighting provides the optimal balance, whereas overly aggressive weighting significantly reduces relevance. The main contribution of this study lies in integrating modern LLM embeddings into a sequential recommendation architecture to improve accuracy and cold-start robustness, while also evaluating the impact of serendipity-oriented re-ranking strategies on balancing recommendation relevance and diversity. Overall, this study demonstrates that modern LLM integration can produce a smarter, more adaptive, and more balanced library recommendation system in terms of both accuracy and serendipity.
Co-Authors Abdul Rezha Efrat Najaf Achmad Fauzi Aghni Qisthina Al Rahma Agung Brastama Putra Akira Permata Ramadhani Al Rahma, Aghni Qisthina alathoillah, abdul hanif Ana Wati3, Seftin Fitri Ananda Lakunti A Andhyni, Cyntia Prisya Anggy Oktaviana Syafira Anita Wulansari Anita Wulansari, S.Kom., M.Kom Annisa Lusyani Zahra Anwar Sodik, Anwar Aprilia, Eka Fahira AryaRafa, Daud Audrey Septya Rosanti Aufa, Taqiyuddin Ahmad Al Bagus Utomo Basma Eno Ketherin Brahmantio Widyo Trenggono Daniar, Ivan Faiz Devi, Ditha Lozera Dewi Safitri, Triyatul Dharmawan, Ega Dhian Satria Yudha Kartika Diana Aqidatun Nisa Ditha Lozera Devi Elfaretta, Syifa Saskia Ersamazaya, Rafi Dhafin Fachrurrozy Nurqoulby Fandi, Rico Satria Farhan Setiyo Darusman Farhan Setiyo Darusman Fariska, Rahmah Putri Ferdiansyah, Rizky Fernaldy, Fabiyan Atha Fidyah Salsabila Putri Sillehu Firsttama, Risav Arrahman Fitri, Anindo Saka Hardiartama, Rendi Heni Lusiana Dewi I Gusti Ayu Sri Deviyanti Indira Setia Amalia Indra Fajar Novian Jannatuzzahra, Khoirunisa Ketherin, Basma Eno Kusumantara, Prisa Marga Kusumantara, Prisa Marga M. Rizal Abdullah Rozi Mahanani, Anajeng Esri Edhi Marga Kusumantara, Prisa Marisca Amanda Hidayat Mashita Kustyani Maulana Arrasyid, Nizar Maulana Kharyska Abadi, Muhammad Mochamad Suhri Ainur Rifky Mochammad Fuad Pandji Mohamad Irwan Afandi Muhammad Burhanuddin F Narendra, Efriza Cahya Nilwanda, Leona Elsa Novian, Indra Fajar Nur Rachman Nidhi Suryono, Muhammad Nurisa Rahma Shantika Nurjanti Takarini Oktania Purwaningrum Oktania Purwaningrum Oktania Purwaningrum Pandu Rizki Maulidiah Permatasari, Reisa Pradana, Rhendy May Putra, Satrio Honggonagoro Pramono Putri, Youlan Indira Putu Anggi Suryantari Rafi Purwa Syahputra Rahayu Kartika Sari Raihana Sakhi Aswanda Rendi Panca Wijanarko Rhendy May Pradana Rivaldy, Adriano Femaz Rizka Hadiwiyanti Safitri, Eristya Maya Saka Fitri, Anindo Salma Nabila Seftin Fitri Ana Wati Sembilu, Nambi Sidhi Pamekas, Afu Siti Oktavia Eka Putri Solehudin Al Ayyubi Sudewantoro N M Sulistyowati Sulistyowati Sulistyowati Sulistyowati Tri Diana Rimadhani Tri Luhur Indayanti Sugata Tri Puspa Rinjeni Ubaidillah Fahmi, Rohmat Wahyuni, Eka Dyar Wati , Seftin Fitri Ana Wati, Seftin Fitri Ana Wibisono, Mahendra Priyo Wibowo, Nur Cahyo Wisnu Mukti Darwansah Yudha Yunanto Putra Yudha Yunanto Putra Zahra, Nabila Athifah