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KORISA: Jurnal Kolaborasi Riset Sarjana
Published by Universitas Harapan Bangsa Purwokerto
ISSN : -     EISSN : 30639263     DOI : -
Core Subject :
Jurnal KORISA: Jurnal Kolaborasi Riset Sarjana mencakup, namun tidak terbatas pada, topik-topik berikut: Ilmu Sains dan Teknik: Teknologi informasi, rekayasa/keteknikan, fisika, kimia, biologi, ekologi, dan energi terbarukan. Kesehatan dan Kedokteran: Kesehatan masyarakat, kedokteran, bioteknologi, keperawatan, farmasi, dan teknologi kesehatan. Ilmu Sosial dan Humaniora: Sosiologi, psikologi, antropologi, ekonomi, manajemen, dan bisnis. Ilmu Hukum: Hukum publik dan privat, hukum teknologi, regulasi kesehatan, dan hukum internasional. Ilmu Pendidikan: Inovasi dalam metode pengajaran, kebijakan pendidikan, teknologi pendidikan, dan psikologi pendidikan.
Arjuna Subject : -
Articles 31 Documents
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Algoritma Cocke-Younger-Kasami untuk Sistem Deteksi Kesalahan Pola Kalimat Bahasa Sunda Oktavia Putri Handayani; Sony Kartika Wibisono; Khoirun Nisa
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 1 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 1 Januari 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

This study develops a sentence error detection system for the Sundanese language using the Cocke-Younger-Kasami (CYK) algorithm, implemented through a website platform. The system aims to efficiently and accurately detect errors in the structure of Sundanese sentences. The CYK algorithm is used to analyze sentences based on a predefined context-free grammar (CFG). The system is built using HTML, PHP, and CSS to create the user interface and process the data. Based on testing results, the system successfully identifies both valid and invalid sentence patterns with an accuracy of 94.44%. This research is expected to contribute to the preservation and understanding of the Sundanese language, as well as make it easier for people to correct errors in their everyday communication.
Daya Saing Karet Alam Negara Pengekspor di Pasar Internasional Ely Saragih; Ady Nugroho; Hadi Jayusman; Riska Suryani; Khoirun Nisa; Sony Wibisono; Christosie Wahyudi
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 2 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 2 Mei 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

Natural rubber is a plantation commodity widely cultivated in the ASEAN region, particularly in Indonesia, Thailand, and Malaysia. This study aims to analyze the export competitiveness of HS 400122 natural rubber from these three countries in the international market. The analytical model employed is the Almost Ideal Demand System (AIDS) using the Seemingly Unrelated Regression (SUR) technique. The study utilizes monthly panel data from January 2012 to December 2021, covering export values and export volumes of natural rubber, sourced from Trademap.org. The results show that Indonesia holds the largest export market share compared to Thailand and Malaysia. Own-price elasticity indicates that natural rubber is a normal good, while compensated cross-price elasticity suggests that trade relationships among these countries are characterized by substitutability. Meanwhile, uncompensated cross-price elasticity reveals both substitutive and complementary relationships among them. These findings provide an overview of the competitive positions of each country and can serve as a reference for policymakers in enhancing the export performance of natural rubber in the international market.
Rancang Bangun Sistem Pemantauan dan Kendali Kelembaban Lingkungan pada Pertanian Perkotaan Dundun Mare; Sriyadi Sriyadi; Aditia Hamid; Rofingi Aji; Rosyid Al-Hakim; Fajarani Fitriasih; Lugito Nurwahono; Agung Pangestu
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 2 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 2 Mei 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

The busy lifestyle of urban residents often limits their time and opportunities to engage in gardening or farming activities at home. To address this, an automated plant irrigation system for vertical gardens was developed based on the Internet of Things (IoT) with the aim of facilitating gardening activities at home. The system employs an ESP32 microcontroller as the main controller, equipped with a soil moisture sensor, a temperature sensor, and the Blynk application for real-time monitoring accessible via Android devices or a web interface. Testing was carried out by comparing sensor readings with standard measuring instruments to determine accuracy, as well as functional testing of the monitoring and control system. The system was designed to maintain soil moisture within a range of 40–60% and the ambient temperature around the plants within 25–35°C. Experimental results showed an average error of 4.29% for soil moisture sensor readings and 5.69% for temperature sensor readings. Furthermore, the monitoring function test using Blynk successfully displayed real-time data with a 100% success rate, ensuring that soil moisture and temperature conditions could be controlled as needed. In the future, this research could be further developed by adding efficiency analysis, economic value assessment, and evaluating the system’s overall power consumption.
Sistem Pendukung Keputusan untuk Klasifikasi Risiko Diabetes Menggunakan Algoritma Decision Tree Hafizah Zuriyat Tayyibah; Toat Tuloh; Khoirun Nisa; Glagah Setyowisnu; Rosyid Al-Hakim; Esa Putri
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 3 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 3 September 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

Diabetes mellitus is a chronic metabolic disease that requires early diagnosis to prevent serious complications. This study aims to develop a diabetes risk classification model using the Decision Tree algorithm, based on a Kaggle dataset consisting of 520 patient records with 17 clinical and demographic features. The evaluation results show that the proposed model performs well, achieving an accuracy of 95.19%, precision of 1.00, recall of 0.93, and an F1-score of 0.96 for the positive class. These results outperform six previous studies that employed algorithms such as C4.5, SVM, Adaboost, KNN, and Naïve Bayes, both in terms of accuracy and sensitivity in detecting diabetes cases. In addition to its strong performance metrics, the study highlights the model’s interpretability through a visualized decision tree, enabling healthcare professionals to better understand the classification logic. With broader attribute coverage and comprehensive performance evaluation, the model is considered effective and suitable for developing accurate and transparent decision support systems for early diabetes diagnosis.
Penerapan Metode Naïve Bayes dengan SMOTE pada Sistem Pendukung Keputusan untuk Prediksi Risiko Stroke Adam Fathurrohman Arya Bakhti; Berliana Rahmadhani; Khoirun Nisa
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 1 No. 2 (2024): Jurnal Kolaborasi Riset Sarjana Volume 1 Nomor 2 Juli 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

Stroke merupakan salah satu penyebab kematian dan kecacatan terbesar di dunia, sehingga prediksi dini menjadi kritis untuk mencegah komplikasi serius. Penelitian ini mengembangkan sistem pendukung keputusan untuk memprediksi risiko stroke menggunakan algoritma Naïve Bayes yang dikombinasikan dengan Synthetic Minority Oversampling Technique (SMOTE) guna mengatasi ketidakseimbangan data pada Stroke Prediction Dataset (5110 sampel, 4,87% kasus stroke). Metode penelitian mencakup preprocessing data, penghapusan fitur non-informatif, encoding variabel kategorikal, oversampling menggunakan SMOTE, serta evaluasi performa model menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa SMOTE meningkatkan sensitivitas model secara signifikan, dengan nilai recall 93% dan F1-score 81%, meskipun precision mengalami penurunan akibat bertambahnya prediksi positif palsu. Temuan ini menegaskan pentingnya pemilihan metrik evaluasi yang tepat pada data tidak seimbang. Studi ini memberikan kontribusi dalam pengembangan pipeline prediksi medis berbasis Naïve Bayes dan menawarkan dasar bagi pengembangan model yang lebih akurat melalui optimasi parameter dan algoritma alternatif.
Peran Viktimologi dalam Reformasi Hukum Pidana di Indonesia Bintang Rizaldy; Calvin Kristianto
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 3 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 3 September 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

This study discusses the role of victimology in Indonesia’s criminal justice system, which has so far been more focused on offenders than on victims. The research employs a normative juridical method with a statute approach and case approach. With the growing awareness of victims’ rights, victimology has emerged as an important discipline to understand the impact of crime on individuals and society. This study examines how victimology can shift existing legal paradigms by emphasizing protection, compensation, and rehabilitation for victims. Through the analysis of recent cases, such as the Kanjuruhan tragedy and the shooting of Pastor Yeremia Zanambani, it was found that the justice system is still not fully responsive to victims’ needs. Policy recommendations include drafting specific victim protection legislation, revising the Criminal Procedure Code (KUHAP), strengthening the Witness and Victim Protection Agency (LPSK), and implementing comprehensive rehabilitation programs. The findings indicate that integrating victimology principles into the legal system can enhance the legitimacy of the judiciary and provide better justice for crime victims.
Analisis Forensik Jaringan pada Router Berbasis Log Menggunakan Metode Live Forensic Fadhilah Dhinur Aini; Ari Peryanto; Yuwono Fitri Widodo
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 2 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 2 Mei 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

The use of network technology is one of the facilities that is easily accessible widely. The router network becomes a link between two devices sending data packets from one network to another. This study uses a simulation with the main laptop device carried out a ping test to the IP network 172.16.10.1 to be connected to the router with the live forensic method aimed at analyzing suspicious data through the router network by accessing the PC client IP address 54.255.213.29 by utilizing the DNS protocol access connected to the wireshark tool to find the results of digital evidence Logs that record in real-time.
Tren dan Tantangan Penerapan Virtual Reality di Bidang Kesehatan Global: Tinjauan Literatur Sistematis Zulkifli; Rian Ardianto
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 1 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 1 Januari 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

Pemahaman kesehatan yang baik adalah kunci untuk mendorong perilaku sehat dan meningkatkan kualitas layanan medis. Namun, metode pembelajaran konvensional sering kali gagal memberikan pengalaman yang imersif dan kontekstual. Virtual Reality (VR) hadir sebagai teknologi transformatif yang menawarkan solusi melalui simulasi tiga dimensi yang realistis, aman, dan interaktif. Penelitian ini bertujuan untuk mensintesis bukti- bukti empiris mengenai efektivitas penggunaan VR dalam meningkatkan pemahaman kesehatan melalui metode regular Literature Review (SLR). Pencarian literatur dilakukan pada base data Scopus, PubMed, ScienceDirect, DOAJ, dan Google Scholar, dengan fokus pada studi yang dipublikasikan dalam sepuluh tahun terakhir. Hasil analisis menunjukkan bahwa VR secara signifikan lebih efektif dibandingkan metode konvensional dalam meningkatkan retensi pengetahuan dan keterampilan prosedural medis. Faktor- faktor kunci yang mempengaruhi keberhasilannya meliputi tingkat imersi, realisme simulasi, umpan balik haptik, dan desain antarmuka yang berpusat pada pengguna. Selain itu, VR terbukti mampu meningkatkan motivasi dan keterlibatan pengguna, baik pada tenaga medis maupun pasien, serta mengurangi kecemasan. Meskipun memiliki potensi besar, implementasi VR masih menghadapi tantangan seperti biaya tinggi, risiko cybersickness, dan kurangnya konten terstandarisasi. Kajian ini menyimpulkan bahwa VR adalah alat yang sangat potensial untuk merevolusi pendidikan kesehatan, namun diperlukan strategi implementasi yang matang dan pengembangan konten yang tervalidasi untuk memaksimalkan dampaknya.
Implementasi Kalkulus Trigonometri pada Pemodelan dan Visualisasi Gelombang Stasioner Menggunakan Python Roziana Fauzun; Rosyid Al-Hakim; Anggit Wirasto; Hadi Jayusman; Glagah Setyowisnu; Tri Famuji
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): Jurnal Kolaborasi Riset Sarjana Volume 3 Nomor 1 Januari 2
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

This study explores the application of calculus concepts in computational modeling and visualization of standing waves in open and closed pipe systems. Standing wave phenomena are fundamental topics in wave physics and are closely related to sinusoidal functions commonly studied in calculus courses, particularly in Informatics education. However, the integration of calculus-based mathematical representations with computational visualization remains limited in undergraduate learning contexts. The objective of this research is to implement trigonometric calculus concepts in modeling sinusoidal standing waves and to visualize their behavior in open and closed pipes using Python-based computation. The research employs a quantitative computational approach, where mathematical wave equations derived from calculus principles are translated into numerical simulations. The study does not involve human participants; instead, data are generated from analytical wave functions and processed through computational visualization techniques using Python. The results demonstrate that the computational models successfully represent the characteristics of standing waves, including nodes and antinodes, in both open and closed pipe configurations. The visualizations clearly illustrate differences in wave patterns resulting from boundary conditions, confirming theoretical expectations from calculus-based wave equations. These findings indicate that computational modeling can effectively bridge abstract calculus concepts and observable physical phenomena. In conclusion, this study contributes a calculus-oriented computational framework that enhances conceptual understanding of standing waves through visualization. The novelty of this research lies in its explicit integration of trigonometric calculus concepts with computational simulation as a pedagogical approach in Informatics-oriented calculus learning.
Analisis Perbandingan Algoritma KNN dan SVM untuk Prediksi Risiko Kesehatan Ibu Hamil Marchelya Soewarno; Rian Ardianto; Riska Suryani; Rosyid Al-Hakim; Christosie Wahyudi
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 3 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 3 September 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

In this study, we compared the performance of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms for predicting pregnancy risk in patients. The dataset used consisted of eight variables, such as number of pregnancies, vaccination status, blood pressure, fetal heart rate, body mass index (BMI), age, and height, which were used to classify pregnancies as high-risk or low-risk. Data preprocessing was performed by standardizing numerical features and dividing the data using a stratified split method (80% training data, 20% test data). Model evaluation results showed that KNN achieved an accuracy rate of 81%, while SVM achieved an accuracy of 75.50%. Further analysis showed that KNN was more stable in classifying data with diverse variable distributions, while SVM showed a tendency to be more sensitive to high-risk cases, with 66.7% of predictions pointing to that category. Compared with previous studies, KNN's performance was within the general accuracy range of 70–95%, while SVM's accuracy was slightly lower than the average of similar studies, which reached around 80.33%. This study concludes that both algorithms have the potential to be tools for early detection of maternal health risks, where the differences in their performance are influenced by the selected parameters, data characteristics, and class division.

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