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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) 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) Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal ilmiah teknologi informasi Asia
Amalia Anjani Arifiyanti
Universitas Pembangunan Nasional “Veteran” Jawa Timur
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Penerapan Algoritma Decision Tree Pada Penentuan Penerima Program Keluarga Harapan di Desa Turirejo, Kedamean Gresik Nilwanda, Leona Elsa; Arifiyanti, Amalia Anjani; Hadiwiyanti, Rizka
Madani: Jurnal Ilmiah Multidisiplin Vol 2, No 1 (2024): Madani, Vol. 2, No. 1 2024
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10523523

Abstract

Based on a press release from the Ministry of Finance's Fiscal Policy Agency, the poverty rate in Indonesia was recorded to have decreased from 9.71% to 9.54% in March 2022. Even though there is pressure on commodity prices, the poverty rate shows a downward trend. Apart from economic growth, Indonesia has several programs launched by the government to alleviate poverty, such as the Family Hope Program. The Family Hope Program is a government social assistance program aimed at poor communities who are designated as beneficiaries of the Family Hope Program. However, in a summary of the audit results for the second semester of 2021, the Financial Audit Authority (BPK) found an error in the allocation of national social assistance (Bansos) which resulted in state losses of up to IDR 6.9 trillion. This is certainly a serious problem, for this reason a system is needed that can assist in the classification process of potential PKH assistance recipients. To carry out classification, you can use the Decision Trend algorithm with the ID3, C45 and Random forest models. To improve model accuracy results, a features selection method is needed. From the results of the classification process, the Random forest model with SMOTE and Features Selection has the highest accuracy of 91% and the system validity test is 91%. From these results, the system has the ability to assist in the classification process of PKH assistance recipients
Analysis Sentiment Of Users Internet Service Providers In Indonesia On Social Media X Using Support Vector Machine Fachrurrozy Nurqoulby; Amalia Anjani Arifiyanti; Dhian Satria Yudha Kartika
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 2 (2024): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v8.i2-16317

Abstract

Various internet service providers are starting to appear in Indonesia, they are competing to provide attractive offers to attract customers. Through social media, someone can find out opinions about whether internet service providers provide services as offered. X, formerly known as Twitter, is a social media platform where people can give their opinions in the form of posts. Various opinions were expressed by the public, ranging from positive, neutral, to negative. This research aims to create a post classification model regarding users of internet service providers into three sentiment classes, namely positive, neutral and negative. The model is created through several stages, such as data retrieval, data labeling, data preprocessing, data division, term weighting, and creating a classification model using the Support Vector Machine algorithm. The results of this research show that the SVM model with a Linear kernel obtained the highest accuracy of 83% compared to the RBF kernel SVM and Polynomial kernel SVM, with an F1-score of 90% for the negative class, 66% for the neutral class, and 65% for the positive class.
RANCANG BANGUN APLIKASI DONOR DARAH DARURAT DONORA BERBASIS ANDROID DENGAN KONSEP GAMIFIKASI MENGGUNAKAN KOTLIN AryaRafa, Daud; Dyar Wahyuni, Eka; Anjani Arifiyanti, Amalia
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.5025

Abstract

Dalam menghadapi permasalahan yang ada di masyarakat terkait keterbatasan pasokan darah darurat, Donora hadir sebagai solusi profesional dan inovatif. Kami mengakui adanya kesulitan yang sering dihadapi oleh masyarakat saat mereka membutuhkan darah darurat dalam situasi darurat seperti kecelakaan atau setelah menjalani operasi besar,salah satu masalah utama yang kami identifikasi adalah kelangkaan stok darah di rumah sakit dan unit transfusi darahAgile Scrum adalah salah satu metode pengembangan produk yang terintegrasi dan berkelanjutan dalam menyelesaikan proyek secara bertahap. Kelebihan utama dari metode ini adalah memungkinkan dengan cepat menyesuaikan dengan perubahan yang mungkin terjadi selama pengembangan produk Ada lima prinsip dari metode pengembangan Agile, yaitu customer involvement, incremental delivery, people not process, embrace change, dan maintain simplicity kami melakukan analisis pesaing untuk menganalisis berbagai fitur yang dapat dikembangkan dalam aplikasi Donora sebagai solusi masalah yang telah diidentifikasi sebelumnya. Dengan melibatkan tim pengembang dan stakeholders, kami menentukan prioritas fitur yang paling penting , memastikan fokus pengembangan pada solusi yang paling efektif danbermanfaat bagi pengguna Donora.Setelah berdiskusi , kami pun membuat product backlog dan menentukan prioritas tiap backlogDalam pengembangan aplikasi donor ini kami menggunakan agile scrum.pada pengembangan mobile apps berbasis android  ini kami menggunakan Bahasa pemrograma kotlin. 
KLASIFIKASI CALON PENDONOR DARAH POTENSIAL MENGGUNAKAN ALGORITMA DECISION TREE DI UTD PMI KOTA SURABAYA Elfaretta, Syifa Saskia; Arifiyanti, Amalia Anjani; Fitri, Anindo Saka
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4957

Abstract

Darah merupakan komponen yang vital dalam tubuh manusia. Kurangnya jumlah darah pada tubuh akan memengaruhi kerja dari organ lain. Oleh karena itu, PMI berperan aktif dalam menyediakan kebutuhan stok darah nasional. Untuk memastikan bahwa darah yang diterima oleh resipien aman dan berkualitas baik, maka perlu dilakukan klasifikasi calon pendonor darah potensial. Penelitian ini menggunakan beberapa algoritma Decision Tree dalam proses klasifikasi data. Algoritma yang digunakan adalah CART, C4.5, dan Random Forest. Hasil perbandingan dari tiga algoritma menunjukkan bahwa Random Forest memiliki nilai terbaik dibandingkan algoritma lainnya. Algoritma Random Forest mendapatkan akurasi dengan nilai 97% dan AUC ROC dengan nilai 99%. Oleh karena itu, algoritma Random Forest diimplementasikan dalam sistem klasifikasi calon pendonor darah potensial berbasis web. Hasil uji validasi sistem menunjukkan akurasi dengan angka 97%.
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.
Penentuan Kelompok Status Gizi Balita dengan Menggunakan Metode K-Means Oktania Purwaningrum; Yudha Yunanto Putra; Amalia Anjani Arifiyanti
Jurnal Ilmiah Teknologi Informasi Asia Vol 15 No 2 (2021): Volume 15 Nomor 2 (8)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v15i2.594

Abstract

Kekurangan gizi pada balita merupakan permasalahan yang masih sering terjadi di Indonesia. Menurut data dari Badan Pusat Statistik (BPS) menunjukkan prevalensi balita kekurangan gizi pada tahun 2018 diatas nilai 10 dan Jawa Timur memiliki nilai 15,20. Permasalahan gizi balita memberikan dampak buruk pada kesehatan balita. Untuk menangani permasalahan kesehatan dan gizi balita hadir adanya posyandu. Posyandu adalah kegiatan yang dilakukan oleh dan untuk masyarakat dibantu oleh petugas kesehatan setempat atau Puskesmas, salah satunya Posyandu Tanjung 1 RW 06 Desa Pepelegi Sidoarjo. Untuk mengelompokan status gizi balita pada Posyandu Tanjung 1 RW 06 Desa Pepelegi Sidoarjo, dilakukan klusterisasi agar penanganan gizi ke balita sesuai sasaran. Proses klasterisasi dilakukan dengan framework CRISP-DM dan algoritma K-Means. Hasil dari analisis klasterisasi menunjukkan bahwa terdapat 3 klaster dari data balita. Diharapkan kader Posyandu menjadikan hasil tersebut menjadi dasar dalam penentuan kebijakan dalam pemberian tindakan seperti vaksinasi dan imunisasi agar sesuai dan tepat guna.
Predicting Software Defects at Package Level in Java Project Using Stacking of Ensemble Learning Approach Zahra, Nabila Athifah; Arifiyanti, Amalia Anjani; Kartika, Dhian Satria Yudha
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1368

Abstract

Compared to manual and automated testing, AI-driven testing provides a more intelligent approach by enabling earlier prediction of software defects and improving testing efficiency. This research focuses on predicting software defects by analyzing CK software metrics using classification algorithms. A total of 8924 data points were collected from five open-source Java projects on GitHub. Due to class imbalance, undersampling was applied during preprocessing along with data cleaning and normalization. The final dataset consists of 1314 instances (746 clean and 568 buggy). The predictive model is developed in two stages: base learner (level-0) using AdaBoost, Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), Histogram-based Gradient Boosting (HGB), XGBoost (XGB), and CatBoost (CAT) algorithms, and meta-learner (level-1) that optimizes the results using ensemble stacking techniques. The stacking model achieved an ROC-AUC score of 0.8575, outperforming all individual classifiers and effectively distinguishing defective from non-defective software components. The comparison of performance improvements between the base model (tree-based ensemble) and stacking was statistically validated using paired t-tests. All p-values were below 0.05, confirming the significance of Stacking’s superior performance, with the largest gain observed against Gradient Boosting (+0.0411, p = 0.0030). The confusion matrix of stacking model is the most optimal model because it has high of True Positive and True Negative, while  False Positive and False Negative values are relatively low. These findings affirm that ensemble stacking yields a more robust and balanced classification system, enhancing defect prediction accuracy and enabling earlier issue detection in the Software Development Life Cycle (SDLC). 
Aspect-Based Sentiment Analysis on User Perceptions of OVO using Latent Dirichlet Allocation and Support Vector Machine Aprilia, Eka Fahira; Arifiyanti, Amalia Anjani; Sembilu, Nambi
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 2 (2025): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i2.3035

Abstract

The rapid development of digital technology and the Internet has significantly influenced financial services in Indonesia, leading to the widespread use of digital wallets. One of the most prominent digital wallet platforms is OVO, which has received millions of user reviews across application stores. This study applies aspect-based sentiment analysis to better understand user perceptions from reviews of the OVO application (versions 3.115 to 3.119). A total of 17.086 reviews were collected through web scraping and refined to 4.996 relevant entries. Topic modeling using Latent Dirichlet Allocation (LDA) identified four main aspects frequently discussed by users: Transaction Efficiency, User Experience, Account Access and Registration, and Balance and Charges. However, automatic aspect labeling using LDA keywords achieved only 11.46% agreement with manual annotations, increasing to 40.60% after keyword refinement. Therefore, manual aspect annotation was adopted as the basis for sentiment labeling. Sentiment labeling was conducted by three annotators based on structured guidelines, achieving a Fleiss’ Kappa score of 0.9915. A classification model was then developed using the Support Vector Machine (SVM) algorithm across six testing scenarios. The best-performing model, using a Linear kernel without ML-SMOTE, achieved a macro-average precision of 0.843, recall of 0.786, and F1-Score of 0.804. These results demonstrate the model’s effectiveness in handling multi-label classification under imbalanced data conditions, particularly for well-distributed aspects such as Transaction Efficiency and User Experience, while highlighting challenges in minority-class detection for aspects such as Account Access and Registration and Balance and Charges.
Enhancing Aspect-Based Sentiment Analysis in Imbalanced Multilabel Datasets using Resampling and Classifiers for Digital Signature Applications Narendra, Efriza Cahya; Arifiyanti, Amalia Anjani; Sugata, Tri Luhur Indayanti
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 2 (2025): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i2.3023

Abstract

Amid the growing demand for digital identity solutions, applications like Privy, VIDA, and Xignature offer integrated digital signature and e-stamp services, generating extensive user feedback on platforms like Google Play Store and App Store. Extracting meaningful insights from thousands of reviews is challenging, necessitating effective sentiment analysis. Aspect-Based Sentiment Analysis (ABSA) enables detailed sentiment evaluation by linking user feedback to specific aspects and sentiments. However, ABSA faces challenges with imbalanced datasets where label distributions are uneven. This study explores the application of three resampling techniques, including MLROS, MLSMOTE, and REMEDIAL, to address this issue in multilabel classification. Using multilabel classifiers, including Binary Relevance, Label Powerset, and Classifier Chains, the study systematically evaluates their performance. Results reveal that resampling significantly enhances outcomes, with MLROS and Classifier Chains under a 70:30 split achieving the best performance, reducing Hamming Loss to 0.0401 or 95% accuracy. This marks a 34.2% improvement over baseline models without resampling or classifiers. The model generalizes well to unseen data with minimal overfitting, as indicated by validation results. These results underscore the importance of imbalanced data resampling and multilabel classification techniques in advancing ABSA, offering valuable insights for improving sentiment analysis in real-world applications.
Analisis Manajemen Risiko Teknologi Informasi pada Dinkominfo Surabaya Menggunakan Metode Failure Mode and Effect Analysis (FMEA) Fidyah Salsabila Putri Sillehu; Marisca Amanda Hidayat; Raihana Sakhi Aswanda; Audrey Septya Rosanti; Agung Brastama Putra; Amalia Anjani Arifiyanti
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 4 (2025): Juli : Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i4.947

Abstract

The use of information technology (IT) in the government sector requires structured risk management to ensure the continuity of public services. The Department of Communication and Information Technology (Dinkominfo), as a digital service provider, faces various potential risks such as network disruptions, hardware failures, and cyberattacks that could interfere with daily operations. This study employs the Failure Mode and Effect Analysis (FMEA) method to identify, analyze, and formulate strategies to mitigate existing IT risks. Through the FMEA approach, each potential failure is evaluated based on its severity, occurrence, and detection capability, which are then used to calculate a Risk Priority Number (RPN). The analysis reveals that the highest RPN values are associated with information system errors, hardware failures, and network overloads. As mitigation measures, the study recommends conducting regular system audits, upgrading network capacity, and performing preventive maintenance on devices. This approach demonstrates that FMEA is an effective method for managing IT risks within government institutions.
Co-Authors Abdul Rezha Efrat Najaf Achmad Fauzi Afandi, Mohamad Irwan Aghni Qisthina Al Rahma Agung Brastama Putra 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 Bagus Utomo Basma Eno Ketherin Brahmantio Widyo Trenggono Brastama Putra, Agung Daniar, Ivan Faiz Devi, Ditha Lozera Dewi Safitri, Triyatul Dewi, Heni Lusiana Dharmawan, Ega Dhian Satria Yudha Kartika Diana Aqidatun Nisa Ditha Lozera Devi Eka Putri, Siti Oktavia Elfaretta, Syifa Saskia 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 I Gusti Ayu Sri Deviyanti Indira Setia Amalia Indra Fajar Novian Irwan Afandi, Mohamad Jannatuzzahra, Khoirunisa Ketherin, Basma Eno Kusumantara, Prisa Marga Kusumantara, Prisa Marga Luhur Indayanti Sugata, Tri 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 Cahyo Wibowo 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 Raihana Sakhi Aswanda Rendi Panca Wijanarko Rhendy May Pradana Rizka Hadiwiyanti Saka Fitri, Anindo Salma Nabila Seftin Fitri Ana Wati Sembilu, Nambi Sidhi Pamekas, Afu Solehudin Al Ayyubi Sudewantoro N M Sugata, Tri Luhur Indayanti Sulistyowati Sulistyowati Sulistyowati Sulistyowati Tri Diana Rimadhani Tri Luhur Indayanti Sugata Ubaidillah Fahmi, Rohmat Wahyuni, Eka Dyar Wati , Seftin Fitri Ana Wati, Seftin Fitri Ana Wibisono, Mahendra Priyo Wisnu Mukti Darwansah Yudha Yunanto Putra Yudha Yunanto Putra Zahra, Nabila Athifah