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IMPLEMENTASI AGILE PADA PENGEMBANGAN SISTEM INFORMASI E-OSSA DI PENGADILAN NEGERI TASIKMALAYA Anggraini, Recha Abriana; Silvi Purnia, Dini; Padilah, Amar; Apriyani, Yanti
CONTEN : Computer and Network Technology Vol. 4 No. 2 (2024): Desember 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/conten.v4i2.7559

E-OSSA merupakan website yang internal yang mana website ini hanya dipergunakan di sekitar wilayah kantor pengadilan negeri tasikmalaya. Website ini adalah website yang digunakan untuk memesan barang barang ATK yang akan digunakan di berbagai bidang yang ada di kantor pengadilan negeri tasikmalaya. Setelah melakukan wawancara pada beberapa bidang terkait yang ada di pengadilan negeri tasikmalaya , salah satu aspek yang paling relevan yang menjadi keluhan utama adalah kesulitan dalam pengoperasian sistem yang dihadapi oleh pengguna. Dalam hal ini, banyak bidang terkait mengungkapkan bahwa mereka menghadapi kendala dalam memahami dan menguasai cara menggunakan sistem tersebut karena tampilan antarmukanya yang membingungkan. tujuan dari tugas akhir ini penulis akan mengembangkan Sistem Informasi E-OSSA yang dapat membantu pengadilan dalam melakukan pemesanan dan pengelolaan persediaan ATK secara terintegrasi dan efektif menggunakan metode agile yang dalam setiap iterasinya, akan melakukan perencanaan, analisis, desain, implementasi, dan pengujian terhadap fitur-fitur yang akan dikembangkan. manfaat dan hasil dari perancangan pengembangan sistem Eossa di pengadilan ini dapat meningkatkan efisiensi dan produktivitas dalam proses pemesanan dan pengelolaan persediaan ATK, sehingga dapat meningkatkan kualitas pelayanan.

Rancang Bangun Sistem Monitoring dan Kendali Komputer Client Berbasis Teknologi Internet of Things Imam Amirulloh; Yanti Apriyani; Melisa Winda Pertiwi; Recha Abriana Anggraini
CESS (Journal of Computer Engineering, System and Science) Vol. 10 No. 1 (2025): Januari 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v10i1.65875

Pemakaian Komputer terutama di perkantoran dan laboratorium yang mempunyai komputer yang cukup banyak menyebabkan pemakaian komputer tidak terkontrol, seperti kebiasaan tidak mematikan Komputer setelah digunakan, sehingga menyebabkan pemborosan energi listrik, mempercepat kerusakan pada Komputer, dan meningkatkan biaya operasional. Kebutuhan akan adanya pemantauan dan pengendalian komputer dari jarak jauh secara realtime sangat diperlukan bagi operator atau pengelola komputer tersebut untuk menunjang efesiensi kerja serta meminimalkan risiko pada konsumsi energi yang berlebihan, potensi kerusakan perangkat keras, dan pembengkakan biaya operasional akibat penggunaan yang tidak terkontrol. Penelitian ini bertujuan untuk menghasilkan sistem pemantauan dan kendali komputer yang dapat dikendali dari jarak jauh menggunakan smartphone. Penelitian ini menggunakan metode prototype. Hasil dari penelitian ini adalah telah dibangun sebuah sistem pemantauan dan kendali pada komputer berbasis teknologi Internet of Things (IoT). Selain itu hasil pengujian menggunakan blackbox testing terhadap 6 komputer klien menunjukan bahwa sistem berfungsi dengan baik, sehingga sistem tersebut dapat memudahkan pengelola dalam memantau dan mengendalikan komputer secara realtime dari jarak jauh.

Sistem Informasi Penjualan Sparepart Motor pada Toko Ketapang Motor Margasari Wati, Fanny Fatma; Anggraini, Recha Abriana; Hidayati, Nadiyah; Maulidah, Mawadatul
Jurnal Sistem Informasi Akuntansi (JASIKA) Vol. 5 No. 1 (2025): Mei 2025
Publisher : LPPM UBSI Kampus Kota Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jasika.v5i1.9094

Developments in the increasingly rapid technological era have resulted in business people encouraging their companies to run more effectively along with the times. This is done in order to maximize profits. One technology that is often looked at is information system technology in the form of websites. The Ketapang Motor Shop, which is a motorbike spare parts and accessories trading business, has several problems including the sales report section, stock reports that are not recorded and not filed every month so that existing data is often lost. In sales transactions, payments are still made using notes so that the calculation and inventory of goods is not yet effective. Therefore, a website-based sales information system was created using the waterfall system development method to overcome this problem. The website was created using several programming languages, namely HTML, PHP, CSS, Bootstrap, and Javascript with MySQL as the data storage database. The existence of this website will help companies, especially in the recording process at the cashier so that the quality of the information presented is more accurate.

Optimalisasi Algoritma Deep Learning Menggunakan Deteksi Tepi Pada Identifikasi Varietas Ikan Nila Bahri, Saeful; Adiwisastra, Miftah Farid; Anggraini, Recha Abriana; Sutisna, Herlan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 1: April 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i1.1578

Tilapia is a fish species that is considered invasive throughout the world, but has high economic value in Indonesia because of its environmental resistance and popularity. There are 12 superior varieties of tilapia that want to be identified in this research. Because the shape of the fish's body, fins, head and other physical characteristics are similar to each other, errors often occur in the process of identifying the type of tilapia when done by humans. Therefore, this research will use computational methods to assist in identifying fish morphology by identifying the morphology or physical characteristics of 12 varieties of tilapia and overcoming errors that often occur in the identification process by applying the edge detection method. This technique will improve image quality and identification accuracy through the Deep Learning algorithm, by optimizing the feature extraction process in the Deep Learning algorithm through edge detection, proven to be 0.9 but the lowest accuracy value was obtained in sobel edge detection combined with LSTM, namely 0.12. It is hoped that this research will increase accuracy and efficiency in identifying the morphology of tilapia fish. This will help experts carry out morphological analysis of tilapia fish more quickly and effectivelyKeywords: Oreochromis niloticus; Deep learning; Identification; Image processingÂ AbstrakIkan nila adalah spesies ikan yang dianggap invasif di seluruh dunia, tetapi memiliki nilai ekonomis yang tinggi di Indonesia karena ketahanannya terhadap lingkungan dan popularitasnya. Ada 12 varietas unggul ikan nila yang ingin diidentifikasi dalam penelitian ini, karena bentuk tubuh ikan, sirip, kepala dan ciri fisik lainnya mirip satu sama lain maka sering terjadi kesalahan dalam proses identifikasi jenis ikan nila jika dilakukan oleh manusia. Oleh karena itu penelitian ini akan menggunakan metode komputasi untuk membantu dalam identifikasi morfologi ikan dengan untuk melakukan identifikasi morfologi atau ciri fisik dari 12 varietas ikan nila serta mengatasi kesalahan yang sering terjadi dalam proses identifikasi dengan menerapkan metode deteksi tepi. Teknik ini akan meningkatkan kualitas citra dan akurasi identifikasi melalui algoritma Deep Learning, dengan mengoptimalkan proses ekstraksi ciri pada algoritma Deep Learning melalui deteksi tepi, terbukti 0,9 namun nilai akurasi terendah didapatkan pada deteksi tepi sobel yang digabungkan dengan LSTM yaitu sebesar 0,12. Penelitian ini diharapkan akan meningkatkan akurasi dan efisiensi dalam identifikasi morfologi ikan nila. Ini akan membantu para ahli dalam melakukan analisis morfologi ikan nila secara lebih cepat dan efektif.Â

IMPLEMENTASI AGILE PADA PENGEMBANGAN SISTEM INFORMASI E-OSSA DI PENGADILAN NEGERI TASIKMALAYA Anggraini, Recha Abriana; Silvi Purnia, Dini; Padilah, Amar; Apriyani, Yanti
CONTEN : Computer and Network Technology Vol. 4 No. 2 (2024): Desember 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/conten.v4i2.7559

E-OSSA merupakan website yang internal yang mana website ini hanya dipergunakan di sekitar wilayah kantor pengadilan negeri tasikmalaya. Website ini adalah website yang digunakan untuk memesan barang barang ATK yang akan digunakan di berbagai bidang yang ada di kantor pengadilan negeri tasikmalaya. Setelah melakukan wawancara pada beberapa bidang terkait yang ada di pengadilan negeri tasikmalaya , salah satu aspek yang paling relevan yang menjadi keluhan utama adalah kesulitan dalam pengoperasian sistem yang dihadapi oleh pengguna. Dalam hal ini, banyak bidang terkait mengungkapkan bahwa mereka menghadapi kendala dalam memahami dan menguasai cara menggunakan sistem tersebut karena tampilan antarmukanya yang membingungkan. tujuan dari tugas akhir ini penulis akan mengembangkan Sistem Informasi E-OSSA yang dapat membantu pengadilan dalam melakukan pemesanan dan pengelolaan persediaan ATK secara terintegrasi dan efektif menggunakan metode agile yang dalam setiap iterasinya, akan melakukan perencanaan, analisis, desain, implementasi, dan pengujian terhadap fitur-fitur yang akan dikembangkan. manfaat dan hasil dari perancangan pengembangan sistem Eossa di pengadilan ini dapat meningkatkan efisiensi dan produktivitas dalam proses pemesanan dan pengelolaan persediaan ATK, sehingga dapat meningkatkan kualitas pelayanan.

Perancangan Sistem Informasi Akuntansi Penjualan Handphone pada Toko Java Phone Fanny Fatma Wati; Andrian Eko Widodo; Nadiyah Hidayati; Mawadatul Maulidah; Recha Abriana Anggraini
Jurnal Sistem Informasi Akuntansi (JASIKA) Vol. 6 No. 1 (2026): Mei 2026
Publisher : LPPM UBSI Kampus Kota Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jasika.v6i1.12701

The rapid development of information technology encourages business actors to improve the quality of data management, including in the field of mobile phone sales. Java Phone Store still uses a manual system for recording sales transactions, which has the potential to cause recording errors, delays in report preparation, and less accurate financial information. Therefore, it is necessary to design an Accounting Information System for Sales that can overcome these problems and improve operational efficiency.This study aims to design a computerized sales accounting information system for Java Phone Store. The research methods used include observation, interviews, and literature study to collect relevant data. The system development method uses the waterfall model, which consists of requirement analysis, system design, implementation, and system testing stages.The results show that the designed system is able to manage product data, sales transactions, and generate sales reports automatically, quickly, and accurately. With this system, data processing becomes more efficient and minimizes errors that occur in the manual system. In conclusion, the implementation of this sales accounting information system is important to improve operational performance and the quality of financial information at Java Phone Store.

Explainable Machine Learning Framework for Thyroid Cancer Recurrence Prediction Tuti Alawiyah; Taufik Wibisono; Recha Abriana Anggraini; Bambang Kelana Simpony; Yesti Siti Nurjanah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8894

Accurate prediction of thyroid cancer recurrence is essential for improving long-term patient management and supporting evidence-based clinical decision-making. Although machine learning has demonstrated promising predictive performance, limited model interpretability remains a major barrier to its clinical adoption. This study aims to develop an Explainable Machine Learning framework for thyroid cancer recurrence prediction by integrating Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) using clinicopathological features. A publicly available dataset containing 383 patient records was preprocessed through label encoding, correlation analysis, Chi-Square-based feature selection, and Min-Max normalization. Logistic Regression, Decision Tree, Random Forest, and XGBoost were comparatively evaluated using 10-fold stratified cross-validation with Accuracy, Precision, Recall, F1-score, and ROC-AUC as evaluation metrics. The best-performing model was subsequently interpreted using global and local SHAP analyses. XGBoost achieved the highest performance, with an accuracy of 95.8% ± 4.4%, precision of 93.4% ± 8.3%, recall of 91.4% ± 9.9%, F1-score of 92.2% ± 8.3%, and ROC-AUC of 98.6% ± 2.5%, outperforming the other models. SHAP analysis identified Response, Risk, and N Stage as the most influential clinicopathological factors affecting recurrence prediction. This study contributes by developing a unified Explainable Machine Learning framework that integrates comparative model evaluation, XGBoost prediction, and global and local SHAP interpretation within a single workflow. The proposed framework provides accurate and clinically interpretable recurrence prediction, supporting trustworthy risk assessment and personalized decision-making in thyroid cancer management.

Klasifikasi Jenis Kismis Menggunakan Teknik Data Mining Recha Abriana Anggraini; Ratningsih; Yanti Apriyani; Melisa Winda Pertiwi; Mira Kusmira; Saeful Bahri
Jurnal Kajian Ilmiah Vol. 24 No. 1 (2024): January 2024
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/ryvqk945

Raisins are one of the processed grape products that are often found in the market, raisins are one of the processed grape products by drying. The color and quality of raisins are usually determined by the type of grape and the drying process. To assess the quality of raisins, many methods can be used, one of which is the traditional method carried out by humans manually. However, since traditional methods are considered to tend to take a long time and errors often occur due to human error. Currently, machine vision systems can be used to assess the quality of raisins. In addition to assessing the quality of raisins, this method can also be used to identify and classify raisins. One way to classify raisins is to use data mining with classification algorithms. This research applies 5 data mining classification algorithms namely naïve bayes, decision tree, random forest, neural network and SVM. From the modeling results of the five algorithms, the neural network algorithm has the highest accuracy of 86.81%.

Algoritma Naïve Bayes Dengan Backward Elimination Pada Dataset Breast Cancer Recha Abriana Anggraini
Jurnal Kajian Ilmiah Vol. 23 No. 1 (2023): January 2023
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/tzsb5v61

Cancer is a type of disease that is not recognized by most people, because some people affected by this disease do not know about cancer itself and do not do early detection of cancer, as a result most cancers are found at an advanced stage and are difficult to treat, thus placing a large burden on cancer sufferers. . Early detection of cancer, especially breast cancer is very important to do to overcome the very high risk of death in women caused by breast cancer. This study aims to help classify breast cancer based on data from routine patient examinations which are summarized in the coimbra breast cancer dataset and this data was donated to the UCI machine learning repository in 2018. The method used in the classification process in this study is backward elimination modeling for optimization accuracy as well as the naive Bayes algorithm and split validation validation to validate the model. The results of this study show an accuracy of 77.14%. These results indicate that the results of this study are good enough to help classify breast cancer.

ANALISIS SENTIMEN ULASAN FREE FIRE MENGGUNAKAN NAIVE BAYES LOGISTIC REGRESSION Aditya Bachtiar; Muhammad Jabbar Hawali; Nicolas Clinton Simbolon; Denis Ardan Maulana; Recha Abriana Anggraini
Journal of Information System Management (JOISM) Vol. 7 No. 2 (2026): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2026v7i2.2433

Abstraksi Penelitian ini dilatarbelakangi oleh meningkatnya volume ulasan pengguna game Free Fire di Google Play Store yang berpotensi menjadi sumber evaluasi penting bagi pengembang, namun memiliki permasalahan ketidakseimbangan kelas sentimen. Tujuan penelitian adalah membandingkan kinerja algoritma Naïve Bayes dan Logistic Regression dalam analisis sentimen ulasan berbahasa Indonesia. Dataset terdiri dari 12.574 ulasan periode Januari 2024–Juni 2025 yang dikumpulkan melalui web scraping dan diberi label biner berdasarkan rating bintang, dengan dominasi sentimen positif sebesar 86,8%. Proses praproses meliputi case folding, normalisasi emoji dan URL, penghapusan stopword bertema game, stemming bahasa Indonesia, serta ekstraksi fitur TF-IDF n-gram (1,2) yang dioptimalkan menggunakan GridSearchCV 10-fold. Evaluasi difokuskan pada akurasi, precision, recall, dan F1-score untuk mengakomodasi ketidakseimbangan kelas. Hasil menunjukkan Logistic Regression unggul dengan akurasi 81,63% dan F1-score 87,91%, sedangkan Naïve Bayes memiliki recall tinggi namun akurasi lebih rendah. Temuan ini menegaskan efektivitas Logistic Regression untuk analisis sentimen ulasan game skala besar di Indonesia.

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