Article Per Year (5 Year)
p-Index From 2021 - 2026
1.959
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Edu Komputika Journal JOIV : International Journal on Informatics Visualization SINTECH (Science and Information Technology) Journal Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization JOMPA ABDI: Jurnal Pengabdian Masyarakat Jurnal Medika: Medika
Kusuma, Bagus Adhi
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Evaluasi Aplikasi Raileo Melalui Analisis Sentimen Ulasan Playstore Dengan Metode Naive Bayes Junianto, Haris; Arsi, Primandani; Kusuma, Bagus Adhi; Saputra, Dhanar Intan Surya
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1505

Abstract

Abstrak The Raileo application is a staffing platform owned by PT. KAI, functions as a personnel data management system. Effective application development requires data as a basis, and one source of data that can be utilized is user reviews. User reviews provide valuable information regarding application performance, user needs, and security aspects. However, challenges arise in managing review data which often contains sarcasm, creating ambiguous meaning and lowering accuracy levels. This research proposes a solution by applying sentiment analysis using Naive Bayes logarithms to 1047 Raileo review data. This method produces an accuracy rate of 94%, with positive and negative sentiment classification. The research results show the words that appear most frequently in Raileo reviews, such as "eror", "sulit", "titik presensi", "titik absen", "titik lokasi", "bug", "lemot," "gagal", "mantap", "bagus", "oke", "mudah", "mempermudah", "mantul", "lengkap","keren","ok", "inovatif", "inovasi", "semoga", "sukses", dan "membantu". These words can be used as a key to analyze all the sentiments contained in the review. In addition, this research identifies "presence point" as the highest negative sentiment word that needs attention in further development. From this sentiment analysis research, the Raileo application produces the highest sentiment value, namely positive sentiment
Classification of Rice Plant Disease Image Using Convolutional Neural Network (CNN) Algorithm based on Amazon Web Service (AWS) Anggraini, Nova; Kusuma, Bagus Adhi; Subarkah, Pungkas; Utomo, Fandy Setyo; Hermanto, Nandang
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5883

Abstract

− In agriculture, rice plays an important role in the Indonesian economy. Rice produces rice, one of the most widely consumed staple food sources in Indonesia. Many factors can cause rice production failure, one of which is leaf pests and diseases. Therefore, early identification and management of plant diseases is an important step in an effort to increase crop yields and ensure food safety. One way to detect rice leaf images early is to perform an image classification process and create a web-based application. The method that has the ability in image processing is deep learning technique with convolutional neural network (CNN) method. The Convolutional Neural Network (CNN) method works to perform and predict diseases in plants by using image categorization or object images. This research aims to apply the web application of image classification of rice plant diseases to the Amazon Web Service (AWS) by identifying and classifying various types of rice leaf diseases using the CNN algorithm, so that farmers can detect rice plant diseases quickly and accurately through image analysis. This application was created using Convolutional Neural Network (CNN) methodology and Software Development Life Cycle (SDLC). The result of this study is that researchers created a web application for the classification of rice plant diseases through leaf images which are divided into 4 categories, namely Healthy, Leaf Blight, Brown Leaf Blight and Hispa, which is made a classification model using CNN with an accuracy value of 0. 8608, then using the streamlit framework to build a website, and utilizing AWS services in the form of Amazon Elastic Compute Cloud (Amazon EC2) as a hosting service, Amazon Simple Storage Service (Amazon S3) as a service for storing rice plant disease classification models and for storing web files, and Amazon Identity and Access Management Role (Amazon IAM) as a service to create a role that gives permission to connect between AWS S3 and AWS EC2. Testing the disease classification model in rice plants implemented on the web in EC2 shows quite good results with an accuracy of 78.5%. This can affect the model's ability to recognize specific disease patterns
Efektivitas Algoritma Random Forest, XGBoost, dan Logistic Regression dalam Prediksi Penyakit Paru-paru Putra, Bernardus Septian Cahya; Tahyudin, Imam; Kusuma, Bagus Adhi; Isnaini, Khairunnisak Nur
Techno.Com Vol. 23 No. 4 (2024): November 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i4.11705

Abstract

Penyakit paru-paru, seperti pneumonia dan kanker paru-paru, menjadi masalah kesehatan global dengan tingkat kematian tinggi, terutama dipengaruhi oleh polusi udara, infeksi, dan kebiasaan merokok. Pencegahan dan deteksi dini sangat penting dalam mengurangi dampaknya. Algoritma yang digunakan dalam penelitian ini meliputi Random Forest, XGBoost, dan Logistic Regression. Tujuannya yaitu untuk membandingkan performa tiga algoritma machine learning dalam mengklasifikasikan penyakit paru-paru menggunakan metrik evaluasi seperti, akurasi, presisi, recall, dan F1-score.  Setelah hyperparameter tuning, XGBoost menunjukkan hasil terbaik dengan akurasi 94,44%, presisi 94,98%, recall 94,44%, dan F1-score 94,41%, menunjukkan keseimbangan optimal antara presisi dan recall. Random Forest juga memberikan hasil yang sebanding dengan XGBoost dengan akurasi dan presisi yang tinggi. Sementara itu, Logistic Regression menunjukkan keterbatasan dalam menangani data yang kompleks, dengan performa yang lebih rendah pada seluruh metrik evaluasi. Penelitian ini menunjukkan bahwa algoritma berbasis pohon keputusan seperti XGBoost dan Random Forest lebih unggul untuk klasifikasi penyakit paru-paru, menjadikannya metode yang lebih andal untuk mendukung deteksi dini penyakit ini.   Kata kunci: Hyperparameter Tuning, Logistic Regression, Penyakit Paru-paru, Random Forest, XGBoost.
Penanganan Imbalanced Dataset untuk Klasifikasi Komentar Program Kampus Merdeka Pada Aplikasi Twitter Magnolia, Cindy; Nurhopipah, Ade; Kusuma, Bagus Adhi
Edu Komputika Journal Vol 9 No 2 (2022): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v9i2.61854

Abstract

Imbalanced dataset merupakan hal yang sering ditemukan secara alami dalam proses penambangan data. Kondisi ini sangat mempengaruhi keakuratan klasifikasi data seperti yang terjadi dalam klasifikasi komentar program Kampus Merdeka yang peneliti lakukan. Penelitian ini akan fokus pada penanganan Imbalanced dataset untuk meningkatkan kinerja klasifikasi komentar yang berasal dari aplikasi Twitter. Data diklasifikasikan ke dalam empat kelas yaitu kelas 0 (untuk informasi), kelas 1 (untuk opini), kelas 2 (untuk pertanyaan), dan kelas 3 (untuk out of topic). Metode yang digunakan untuk balancing dataset adalah Undersampling, Oversampling menggunakan SMOTE dan ADASYN, serta Random Combination Sampling. Evaluasi performa dilakukan menggunakan algoritma Support Vector Machine (SVM) dengan perbandingan komposisi data training dan testing 80:20. Metode pembobotan data yang digunakan adalah Term Frequency-Inverse Document Frequency (TF-IDF) dengan nilai max_features 3000, 5000, dan 7000. Hasil pengujian awal menunjukan bahwa nilai akurasi dan F1-score pada Imbalanced dataset secara berurut-urut adalah 0,7 dan 0,7. Sedangkan metode penanganan Imbalanced dataset dapat meningkatkan nilai F1-score, kecuali pada penerapan metode Undersampling. Metode terbaik ditunjukan oleh penerapan ADASYN dengan nilai akurasi dan F1-score berurut-urut sebesar 0,9 dan 0,9. Penggunaan max_features pada TF-IDF juga mempengaruhi hasil performa klasifikasi, dengan max_features terbaik ditunjukan pada jumlah 5000.
Eksplorasi Sentimen Publik terhadap Film "˜Dirty Vote"™ melalui Metode Naïve Bayes dan Logistic Regression Junianto, Haris; Saputro, Rujianto Eko; Kusuma, Bagus Adhi; Saputra, Dhanar Intan Surya
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 10, No 3 (2024): Volume 10 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v10i3.78520

Abstract

Tahun 2024 merupakan tahun politik bagi masyarakat Indonesia, di mana mereka menggunakan hak pilih untuk menentukan pemimpin pemerintahan selama lima tahun ke depan. Dalam konteks ini, pendidikan politik menjadi sangat penting, terutama bagi warga yang kurang memahami seluk-beluk politik dan proses pemilihan umum. Menyadari pentingnya pemahaman tersebut, sekelompok akademisi menciptakan film berjudul "Dirty Vote" dengan tujuan meningkatkan kesadaran masyarakat mengenai proses pemilu serta meminimalisir potensi pelanggaran.Penelitian ini bertujuan untuk mengevaluasi opini publik terkait film "Dirty Vote" dengan menggunakan dua model klasifikasi, yaitu Naive Bayes dan Logistic Regression. Penelitian ini melibatkan beberapa tahap, mulai dari pengumpulan data melalui scraping komentar dari platform YouTube, preprocessing data, analisis eksploratif (Exploratory Data Analysis), hingga pengujian performa model menggunakan teknik K-fold Cross Validation, serta visualisasi data menggunakan Word Cloud. Dalam penelitian ini, sebanyak 8888 data komentar dianalisis menggunakan teknik pemrosesan bahasa alami untuk mengukur sentimen publik terhadap film tersebut. Hasil analisis menunjukkan bahwa algoritma Naive Bayes mengidentifikasi 91,30% sentimen positif dan 8,70% sentimen negatif, sedangkan algoritma Logistic Regression memberikan hasil yang lebih tinggi, dengan sentimen positif sebesar 95,65% dan negatif sebesar 4,35%. Dari segi performa, Logistic Regression terbukti lebih unggul dengan akurasi mencapai 95,5%, sedangkan Naive Bayes memiliki akurasi sebesar 91,1%. Pengujian performa dilakukan melalui satu kali pengujian penuh serta delapan kali pengujian dalam berbagai kondisi data, dengan evaluasi kinerja menggunakan ROC dan AUC. Hasil penelitian ini menunjukkan bahwa kedua algoritma memberikan evaluasi positif terhadap film "Dirty Vote", dengan Logistic Regression memberikan hasil yang lebih akurat.
COMPARISON OF LOGISTIC REGRESSION AND RANDOM FOREST IN SENTIMENT ANALYSIS OF DISDUKCAPIL APPLICATION REVIEWS Junianto, Haris; Saputro, Rujianto Eko; Kusuma, Bagus Adhi; Saputra, Dhanar Intan Surya
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.1802

Abstract

Civil registration administration institutions such as Disdukcapil have an important role in carrying out government functions, in supporting the smooth running of administrative services the Government presents the Disdukcapil Mobile Application platform which aims to provide efficient and fast services to the community regarding various population administration needs. Sentiment analysis of user reviews on the Play Store for the Disdukcapil application is needed to understand user perceptions and needs, as well as to improve service quality and application development. In this study, researchers conducted sentiment analysis using 2 algorithms, namely: Logistic Regression and Random Forest, which after comparing by testing the two algorithms with test data of 18810 user review data from PlayStore, obtained the performance results of each algorithm as follows: 90% accuracy, 91% precision, 89% recall, and f1 90% for the performance results of the Logistic Regression algorithm, while for the performance results of the Random Forest algorithm accuracy 89%, precision 92%, recall 86% and f1-score 89%. From these results the Logical Regression algorithm has better performance than the Random Forest algorithm.
Comparative Analysis of Openpuff and Openstego Tools Heryanti, Linda; Baihaqi, Wiga Maulana; Habibah, Ariska Nurul; Kusuma, Bagus Adhi
JINAV: Journal of Information and Visualization Vol. 5 No. 1 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav2327

Abstract

Steganography is the science and art of hiding information in a medium so that its existence is not detected by unauthorized parties. Media that can be used in steganography are text, images, audio, and video. However, the media that is often used is image. Various steganography tools have been developed with their respective strengths and weaknesses, such as Hide in Picture; Openstego; Image Steganography; Invisible Secret 4; S-Tools; Hide 'N' Send; Online Image Steganography, Openpuff, and others. Researchers carried out a comparative analysis of the steganography tools Openpuff and Openstego with test parameters for the quality of the images produced and time efficiency. Test the quality of the resulting image using MSE, PSNR, NCC, SSIM, and time-efficient testing seen from embedding and extraction time. Based on the research results, show that Openstego has better image quality and time efficiency compared to Openpuff. The type of image format used and the size of the embedded message can affect the quality of the resulting image and the time used. The best test results were obtained, namely MSE=0.0009, PSNR=78.5438 dB, NCC=0.999999, SSIM=0.999993, and required embedding time=0.075 second and extraction time=0.084 second. Keywords: Image Quality, Openpuff, Openstego, Steganography, Time.
IMPLEMENTASI REST API DALAM PENGEMBANGAN BACKEND INVENTORY PEMINJAMAN Farchani, Saofikh Bagus; Hermanto, Nandang; Kusuma, Bagus Adhi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6249

Abstract

Inventory management is a crucial element for organizations that have assets to be monitored and managed effectively. In the era of information technology, web-based inventory systems have become a leading solution, enabling real-time inventory management and enhancing operational efficiency. This study focuses on the implementation of a REST API using Node.js in the development of an inventory system for Balai Desa Widarapayung Kulon. Node.js, with its ability to handle asynchronous requests, offers high efficiency in managing workloads. This study includes the design, development, and implementation of a REST API that connects the frontend application with the backend database, as well as identifying challenges and opportunities in using Node.js in a local environment. Through the Waterfall method, which includes requirement analysis, system design, implementation, integration and testing, deployment, and maintenance, this system is expected to provide optimal and adaptive services. Sustainable maintenance ensures the system remains reliable, secure, and capable of meeting user needs. The results show that this automated inventory system improves operational efficiency, transparency, and accountability in public asset management at Balai Desa Widarapayung Kulon, and provides an example for other local entities in utilizing technology to enhance service quality and asset management.
Implementasi Metode Prototyping pada Perancangan Sistem Pengaduan Kekerasan Siswa SMK Al-Huda Bumiayu Waluyo, Retno; Kusuma, Bagus Adhi; Khasanah, Fitrotul; Nugroho, Rizki
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 4 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i4.44913

Abstract

Education is an important foundation in shaping the character and potential of the younger generation. Some issues that arise in the world of education include cases of violence, bullying, inappropriate behavior, or other problems that disrupt students' comfort at school. SMK Al- Huda Bumiayu, as an educational institution, has a moral and legal responsibility to protect students from all forms of violence, including bullying and inappropriate behavior. The handling so far, in case of violence, can be done by contacting the guidance counselor via WhatsApp or through the suggestion or complaint box for students. Through these two media, a problem arises regarding the data on incidents of violence occurring in the school, which is not well-documented because it is often not recorded in the logbook and the focus is on resolving existing problems. Additionally, the progress of handling these issues is also unknown, making it difficult for the principal to make policies related to handling violence in the school. The objective of this research is to develop a web-based student complaint system using the prototyping method to facilitate the reporting of violence by students in the environment of SMK Al-Huda Bumiayu. The results of this research conclude that the prototyping method can be used to create a violence reporting system for students at SMK Al-Huda Bumiayu and assist students in reporting violence they have experienced or witnessed.
Literasi Digital Penggunaan Media Sosial Dalam Menangani Cyberbullying Di SD Al Izzah Primandani Arsi; Subarkah, Pungkas; Riyanto, Riyanto; Kusuma, Bagus Adhi; Riandini, Dini; Widi Lestari, Tri Endah; Hermanto, Nandang
Jompa Abdi: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2025): Jompa Abdi: Jurnal Pengabdian Masyarakat
Publisher : Yayasan Jompa Research and Development

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57218/jompaabdi.v4i2.1571

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kesadaran dan pemahaman orang tua wali murid SD Al Izzah Purwokerto, Kabupaten Banyumas, terhadap pentingnya media sosial untuk menangani anti cyberbullying, karena saat ini banyak anak-anak atau remaja menjadi korban dan pelaku cyberbullying. Metode yang digunakan dalam kegiatan ini yaitu tahap persiapan, tahap pelaksanaan dan tahap evaluasi. Kegiatan ini diikuti oleh 42 peserta orang tua wali murid, dengan hasil evaluasi mengalami peningkatan secara signifikan. Hal ini dibuktikan dengan adanya pre test dan post test dengan memperoleh hasil mengalami kenaikan 40%. Kegiatan ini direkomendasikan untuk dilanjutkan dengan cakupan permasalahan yang lebih luas dan pelibatan instansi lain. Rekomendasi setelah pelatihan ini yaitu harapannya ada tindak lanjut secara berkala salah satunya kerja sama dengan dinas terkait agar pelatihan sejenis dilaksanakan kembali.
Co-Authors Abu, Nor Azman Adam Prayogo Kuncoro Ahmad, Asmala Anggraini, Nova Arsi, Primandani Baihaqi, Wiga Maulana Dhanar Intan Surya Saputra Ekhsanto, Bagus kurniawan Fandy Setyo Utomo Farchani, Saofikh Bagus Habibah, Ariska Nurul Heryanti, Linda Imam Tahyudin Isnaini, Khairunnisak Nur Junianto, Haris Khasanah, Fitrotul Khofiyah, Salma Ngarifatul Magnolia, Cindy Mohd, Othman Nandang Hermanto Nugroho, Rizki Nurhopipah, Ade Primandani Arsi Purwadi, - Putra, Bernardus Septian Cahya Riandini, Dini Riyanto Riyanto Rujianto Eko Saputro Septiana Putri, Refida Subarkah, Pungkas Waluyo, Retno WIDI LESTARI, TRI ENDAH