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All Journal Teknika Jurnal Sains dan Teknologi Jurnal Simetris JSI: Jurnal Sistem Informasi (E-Journal) International Journal of Advances in Intelligent Informatics IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi WIDYA LAKSANA Jurnal Informatika Kaputama (JIK) EVOLUSI : Jurnal Sains dan Manajemen JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Ilmu Teknik dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Infotek : Jurnal Informatika dan Teknologi JUSTIAN - Jurnal Sistem Informasi Akuntansi J-Intech (Journal of Information and Technology) Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Ilmu Komputer Dan Informatika Jurnal Abdimas Le Mujtamak TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Dedikasi Saintek Jurnal Pengabdian Masyarakat SOROT: Jurnal Pengabdian Kepada Masyarakat Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Jurnal Komputer dan Teknologi (JUKOMTEK) DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat Indonesian Community Service Journal of Computer Science (IndoComs)
Riski Annisa
Universitas Bina Sarana Informatika
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Penggunaan Aplikasi Kenaikan Kelas sebagai Alat Monitoring Proses Belajar Mengajar: The use of the Class Advancement Application as a Tool for Monitoring the Teaching and Learning Process Annisa, Riski; Sabaruddin, Raja; Rahayuningsih, Panny Agustia; Winnaarto, Monikka Nur
Jurnal Abdimas Le Mujtamak Vol. 3 No. 2 (2023): Le MUJTAMAK 2023: Juli - Desember
Publisher : Universitas Islam Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46257/jal.v3i2.818

Abstract

Pengabdian masyarakat ini bertujuan untuk mengintegrasikan aplikasi kenaikan kelas sebagai alat untuk memantau proses belajar mengajar, dengan fokus pada penggunaan teknologi informasi untuk meningkatkan kualitas pendidikan. Ini dilakukan untuk menunjukkan komitmen untuk meningkatkan pendidikan. Permasalahan yang diupayakan untuk diberikan solusinya adalah mengenai implementasi aplikasi untuk dapat membantu pencapaian akademik dan keterlibatan siswa dalam belajar. Tujuan utamanya adalah meningkatkan kinerja guru melalui adaptasi terbaik terhadap kebutuhan siswa dengan visualisasi data yang efektif. Metode yang digunakan dalam kegiatan ini termasuk pengumpulan dan analisis data dalam waktu nyata. Hasil penelitian menunjukkan keberhasilan aplikasi memberikan pengaruh positif pencapaian akademik dan keterlibatan siswa. Selain itu, aplikasi meningkatkan kinerja guru dengan menyesuaikannya dengan kebutuhan unik siswa. Strategi penanganan terbukti berhasil, menunjukkan bahwa aplikasi dapat digunakan dan fleksibel meskipun menghadapi masalah selama implementasi. Diidentifikasi bahwa hal-hal penting untuk pengembangan lebih lanjut adalah meningkatkan keterlibatan orang tua, menciptakan sifat inovatif, dan memberikan pelatihan lanjutan bagi pendidik. Hasil penelitian menekankan bahwa aplikasi kenaikan kelas adalah alat yang efektif untuk meningkatkan kualitas pendidikan dan membuat lingkungan pembelajaran yang dinamis, responsif, dan sesuai dengan zaman.
Rancang Bangun Expert System Diagnosa Penyakit Mata Manusia Menggunakan Metode Certainty Factor Anna, Anna; Annisa, Riski; Rahayuningsih, Panny Agustia; Nurdiani, Siti
Infotek: Jurnal Informatika dan Teknologi Vol. 7 No. 1 (2024): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v7i1.24119

Abstract

When the eyes experience interference, the sufferer will be very uncomfortable in doing their daily activities. In an expert system, the Certainty Factor methods are used to determine the percentage of possible eye diseases suffered and provide accurate diagnostic information based on research conducted. The expert system developed to diagnose eye disease has been successfully implemented using knowledge which includes 22 symptoms and 6 types of related diseases. The purpose of this study is to explore the symptoms displayed in the form of questions in order to diagnose the type of disease with web management system-based software. So that later it gives the results of the percentage of diagnosis of eye disease after based on the symptoms suffered. Of course this research can help experts in dealing with existing problems and make it easier for the general public to get information about eye diseases directly consult through the system without having to check with an eye doctor first, can also provide relief for the underprivileged people who want to consult directly with experts only through Web application.
Aplikasi Praktis Metode Pengembangan Cepat (RAD) Dalam Sistem Manajemen Penjualan Dan Pengembalian Barang Berbasis Web Annisa, Riski; Meilinda, Eva; Yustisio, Sendi
Jurnal Informatika Kaputama (JIK) Vol 8 No 1 (2024): Volume 8, Nomor 1, Januari 2024
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v8i1.418

Abstract

In the process of recording sales transactions at Toko Kurnia Jaya always use a manual typewriter so that it is not effective and inaccurate in recording sales transactions, and sales notes are still manual on paper. Another problem is in the process of returning goods, if the goods purchased by the buyer are not appropriate. From these problems, it is formulated how to build a web-based application for recording sales transactions and returning goods. The data collection method that the author uses in completing this report is the method of observation, interviews and library studies. The system development model that the author uses is the RAD model, the programming language that the author uses is PHP and MySql as a database and tested using the Blackbox testing method. With the application of processing sales transactions and returning goods, it can facilitate the search for goods data and more quickly find information about the price of goods and make it easier to manage transaction data, both knowing the total sales results, returning goods and recording incoming goods data.
PENERAPAN METODE WATERFALL DALAM MERANCANG APLIKASI MOBILE GURU DAN ORANG TUA Annisa, Riski; Baihaqi, Muhammad Rivaldi
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 5 No. 2 (2021): Volume 5, Nomor 2, Juli 2021
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v5i2.543

Abstract

In the current development of educational technology, the world of education cannot be separated from the role of the internet. Mobile devices have become part of human daily life in facilitating activities, currently, the need for mobile-based information technology is very necessary, therefore schools need an information system that can support and provide services between teachers (schools) and parents of students. . This study discusses the application of the waterfall in designing mobile-based teacher and parent applications. Currently, most of the information systems in schools are still done manually, starting from news announcements, school grades, and tuition payments, making it possible for errors to occur during the delivery process. The design of this application is the best solution to solve problems that exist in schools, and with a computerized system, an effective and efficient activity can be achieved in supporting activities at this school.
ANALISIS KOMPARASI ALGORITMA KLASIFIKASI DATA MINING UNTUK PREDIKSI PENDERITA PENYAKIT JANTUNG Annisa, Riski
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 3 No. 1 (2019): Volume 3, Nomor 1, Januari 2019
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v3i1.650

Abstract

Penyakit jantung adalah istilah umum untuk semua jenis gangguan yang mempengaruhi jantung. Penyakit jantung berarti sama dengan penyakit jantung tetapi tidak penyakit kardiovaskular. Penelitian ini akan melakukan perbandingan beberapa algoritma klasifikasi yaitu Decision Tree, Naïve Bayes, k-Nearest Neighbour, Random Forest, dan Decison Stump dengan menggunakan uji parametrik dengan t-test agar dapat menghasilkan perbandingan metode yang lebih baik untuk data set laki-laki penderita Penyakit jantung. Hasil penelitian mendapatkan nilai akurasi sebesar tertinggi sebesar 80.38%. Hasil penelitian menunjukkan bahwa algoritma random forest dan decision stump melakukan performa terbaik dalam pengklasifikasi di dataset, C4.5 dan Naïve bayes juga tampil baik, kemudian k-NN merupakan algoritma yang kurang baik diimplementasikan dalam dataset.
Perbandingan Kinerja Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam Analisis Sentimen Mobile Legends Zikirlah, Hikmawan Alvin; Iltavera Paula; Muhammad Fazilla; Riski Annisa; Lady Agustin Fitriana
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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

Abstract

The rapid advancement of information and communication technology has significantly increased the popularity of online games in Indonesia, one of which is Mobile Legends: Bang Bang (MLBB), with millions of active users. The abundance of user reviews on digital platforms provides valuable data for analysis using text mining and natural language processing (NLP) approaches. Sentiment analysis is applied to classify user opinions into positive, negative, and neutral categories, offering insights into player satisfaction and perceptions of game quality. This study compares the performance of three classification algorithms, Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), in analyzing sentiment from Mobile Legends user reviews on the Google Play Store. A total of 5,000 reviews were collected using the web scraping technique and processed through the Knowledge Discovery in Databases (KDD) framework, which includes cleaning, case folding, tokenization, normalization, and stopword removal. Sentiment labeling was performed using a lexicon-based approach with the InSet sentiment lexicon. The dataset was divided into training and testing sets with an 80:20 ratio and evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm achieved the highest accuracy of 88.1%, followed by KNN at 65.1% and NB at 62.6%. Thus, SVM is recommended as the most effective model for sentiment analysis of Mobile Legends user reviews.
Desain Implementasi Sistem Informasi Akuntansi Kinerja Keuangan menggunakan Scrum di Kantor Pertanahan Kubu Raya Panny Agustia Rahayuningsih; Anna Anna; Riski Annisa
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9374

Abstract

Abstrak - Kantor Pertanahan merupakan lembaga pemerintah yang memiliki tanggung jawab dalam mengelola dan mengatur administrasi pertanahan di suatu daerah. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem informasi akuntansi kinerja keuangan yang lebih efisien dan efektif di Kantor Pertanahan Kabupaten Kubu Raya. Fokus utama penelitian ini adalah untuk mengatasi keterbatasan sistem e-Monev Bappenas yang saat ini digunakan oleh bagian Perencanaan, Evaluasi, dan Pelaporan. Sistem tersebut belum mampu menyesuaikan revisi pagu anggaran secara otomatis serta belum memiliki fitur-fitur penting seperti jurnal umum, buku besar, neraca saldo, dan laporan kas, sehingga menyulitkan dalam menyusun laporan keuangan secara akurat dan efisien. Data dalam penelitian ini dikumpulkan melalui observasi langsung, wawancara, dan kajian literatur. Hasil analisis menunjukkan bahwa sistem keuangan yang digunakan saat ini tidak mampu menyajikan laporan keuangan dengan baik karena tidak mencerminkan perubahan dalam pagu anggaran. Sebagai solusinya, penelitian ini berhasil merancang sebuah aplikasi baru berbasis metode Scrum yang mampu menangani revisi anggaran secara tepat dan dilengkapi dengan fitur-fitur penting seperti jurnal umum dan khusus, buku besar, neraca saldo, laporan laba rugi, laporan perubahan modal, dan laporan kas. Dengan adanya sistem baru ini, Kantor Pertanahan dapat menyusun laporan keuangan dengan lebih akurat dan tepat waktu, yang pada akhirnya meningkatkan efisiensi dan efektivitas dalam pengelolaan keuangannya.Kata kunci: Sistem Informasi Akuntansi; Kinerja Keuangan; Revisi Pagu Anggaran; Kantor Pertanahan;  Metode Scrum; Abstract - The Land Office is a government agency that has the responsibility of managing and regulating land administration in an area. This research aims to design and develop a more efficient and effective financial performance accounting information system at the Kubu Raya Regency Land Office. The main focus of this research is to overcome the limitations of the Bappenas e-Monev system currently used by the Planning, Evaluation and Reporting section. The system has not been able to automatically adjust the revision of the budget ceiling and does not have important features such as general journals, ledgers, balance sheets, and cash reports, making it difficult to prepare financial reports accurately and efficiently. The data in this study were collected through direct observation, interviews, and literature review. The results of the analysis show that the current financial system is not able to present financial reports properly because it does not reflect changes in the budget ceiling. As a solution, this research successfully designed a new application based on the Scrum method that is able to handle budget revisions appropriately and is equipped with important features such as general and special journals, ledgers, trial balance, income statement, statement of changes in capital, and cash statement. With this new system, the Land Office can prepare financial reports more accurately and in a timely manner, which in turn improves efficiency and effectiveness in its financial managementKeywords: Accounting Information System; Financial Performance; Budget Ceiling Revision; Land Office; Scrum Method;
PEMODELAN ANALISIS SENTIMEN ROBLOX MENGGUNAKAN ALGORITMA MACHINE LEARNING Agnes, Veronika; Mutia Sari, Elsa; Annisa, Riski; Agustin Fitriana, Lady
Jurnal Komputer dan Teknologi Vol 5 No 1 (2026): JUKOMTEK JANUARI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i1.505

Abstract

The rapid advancement of digital technology has fostered the rise of various interactive online gaming platforms, with Roblox standing out as one of the most prominent. This platform allows users not only to play but also to design and share their own games. As the number of active users increases, the volume of reviews submitted on the Google Play Store also grows. These reviews contain valuable information but require sentiment analysis to automatically understand users’ opinions, satisfaction levels, and complaints. This research aims to conduct sentiment analysis on Roblox user reviews by comparing the performance of three machine learning algorithms—Naïve Bayes, Random Forest, and Decision Tree—to determine which yields the most optimal results. The study follows the Knowledge Discovery in Databases (KDD) framework, which includes several stages: selecting 5,000 reviews, performing text preprocessing (such as cleaning, case folding, tokenizing, normalization, stopword removal, stemming, and labeling), transforming data using word embedding, and evaluating model performance with metrics including Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The experimental findings indicate that the Decision Tree algorithm achieved the best performance, with an accuracy of 85%, precision of 0.847, recall of 0.850, and a weighted F1-score of 0.848. In contrast, Random Forest obtained an accuracy of 83.6% and a macro F1-score of 0.773, while Naïve Bayes recorded the lowest performance with 64.2% accuracy and a macro F1-score of 0.527. Overall, the Decision Tree algorithm demonstrated superior capability and balance in classifying positive, negative, and neutral sentiments in Roblox user reviews, showing more effective text pattern recognition compared to probabilistic-based methods.
Aplikasi Perkiraan Efisiensi Bahan Bakar Mobil dengan Machine Learning dan Streamlit Famela Jessica; Winny Christiani Thomas; Chania Lista Zepani; Muhammad Eka Fadillah; Riski Annisa
JUKOMIKA (Jurnal Ilmu Komputer dan Informatika) Vol. 8 No. 2 (2025): Desember
Publisher : LPPMPP Yayasan Sejahtera Bersama Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jukomika.v8i2.614

Abstract

Efisiensi dalam pemakaian bahan bakar adalah hal krusial bagi kinerja kendaraan, terutama di Indonesia yang masih tergantung pada sumber energi fosil, sehingga perkiraan penggunaan bahan bakar menjadi sangat penting. Studi ini bertujuan untuk menciptakan model ramalan efisiensi bahan bakar dengan menggunakan Linear Regression dan Neural Network (NN), serta menerapkannya dalam aplikasi Streamlit. Linear Regression dan NN dipilih karena kedua metode ini belum banyak diterapkan pada penelitian tentang konsumsi bahan bakar. Dataset Auto MPG (Miles Per Galon) dari Kaggle digunakan, yang terdiri dari 398 data kendaraan dari tahun 1970 hingga 1982, tanpa nilai yang hilang, meskipun terdapat beberapa pencilan yang perlu diperhatikan. Proses preprocessing melibatkan normalisasi fitur numerik, pengkodean variabel kategori, dan pemisahan data ke dalam set pelatihan dan pengujian. Model NN diatur dengan satu lapisan tersembunyi yang berisi 100 neuron dan dilatih hingga 200 epoch. Temuan penelitian menunjukkan bahwa Linear Regression memberikan hasil terbaik (R² = 0,653; RMSE = 4,207), sementara NN menunjukkan hasil yang kurang memuaskan (R² = 0,052; RMSE = 6,958) karena ukuran dataset yang kecil dan hubungan antar data yang cenderung linear. Aplikasi Streamlit yang dibuat memungkinkan pengguna untuk memasukkan data secara manual dan menyajikan visualisasi sederhana untuk menampilkan prediksi variabel MPG. Penelitian ini menyoroti bahwa Linear Regression lebih cocok untuk digunakan pada dataset Auto MPG dibandingkan dengan Neural Network.
Sistem Prediksi Risiko Penyakit Jantung Berbasis Machine Learning dan Framework Streamlit Hidayana, Reymond Syahputra; Regina, Fransiska; Rendi, Rendi; Annisa, Riski
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.10158

Abstract

Abstrak - Penelitian ini menggunakan algoritma pembelajaran mesin untuk membangun sistem yang dapat memprediksi risiko penyakit jantung. Dalam dataset Cleveland Heart Disease, tiga algoritma Logistic Regression, XGBoost, dan Naive Bayes digunakan dengan pembagian data uji dan latih sebesar 80:20. Pembersihan data, pemisahan fitur dan target, pelatihan model, dan evaluasi menggunakan metrik akurasi, presisi, recall, f1-score, dan AUC dilakukan. Hasil pengujian menunjukkan bahwa Logistic Regression adalah yang terbaik dengan skor akurasi, presisi, recall, dan f1-score sebesar 0,90, dan AUC sebesar 0,94. Selanjutnya, model terbaik diterapkan pada sistem prediksi berbasis web yang menggunakan framework Streamlit. Selain data pengguna, sistem dapat menampilkan risiko penyakit jantung secara informatif. Berdasarkan hasil penelitian, model Logistic Regression dapat digunakan sebagai alat bantu awal dalam mendeteksi risiko penyakit jantung secara efektif.Kata kunci : Prediksi Penyakit Jantung; Machine Learning; Logistic Regression; Klasifikasi; Streamlit; Abstract - This study employs machine learning algorithms to develop a system capable of predicting the risk of heart disease. Using the Cleveland Heart Disease dataset, three algorithms—Logistic Regression, XGBoost, and Naive Bayes—were applied with an 80:20 train-test split. Data cleaning, feature–target separation, model training, and evaluation using accuracy, precision, recall, f1-score, and AUC metrics were conducted. The results indicate that Logistic Regression performs the best, achieving accuracy, precision, recall, and f1-score values of 0.90, and an AUC of 0.94. The best-performing model was then deployed in a web-based prediction system using the Streamlit framework. In addition to user input, the system provides an informative display of heart disease risk. Based on the findings, the Logistic Regression model can serve as an effective preliminary tool for detecting heart disease risk.Keywords: Heart Disease Prediction; Machine Learning; Logistic Regression; Classification; Streamlit;
Co-Authors Agnes, Veronika Agung Sasongko Agustin Fitriana, Lady Anna Anna Anna Anna Anna Anna Anna Anna, Anna Anna, Anna - Arvin Hidayat Waluya Baihaqi, Muhammad Rivaldi Chania Lista Zepani Chika Suhendra Detylia Detylia Dewi Ayu Nur Wulandari Dharmawan, Weiskhy Steven Didi Rosiyadi Dwiza Riana Eliska, Puspa Eva Meilinda Fadilah, Aldi Famela Jessica Firly Annisa Luthfi Hidayana, Reymond Syahputra Iltavera Paula Lady Agustin Fitriana Lestari Yusuf Marelita Marelita Maulana, Reza - Mohammad Kamal Reza Monikka Nur Winnarto Muhammad Eka Fadillah Muhammad Fazilla Murdiansyah Murdiansyah Mutia Sari, Elsa Nur W, Monikka Nurfia Oktaviani Syamsiah Panny Agustia Rahayuningsih Parwandar, Robi Rahayuningsih, Agustia Raja Sabaruddin Raja Sabaruddin Raja Sabarudin Ramadhani, Zulfikar Ismaya Regina, Fransiska Rendi Rendi, Rendi Robi Parwandar Sendi Yustisio Siti Nurdiani Sri Murni Suhendra, Chika Wahyu Nugraha Welly, Welly Winnaarto, Monikka Nur Winny Christiani Thomas Yulia Yulia Yustisio, Sendi Zikirlah, Hikmawan Alvin