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All Journal Sistemasi: Jurnal Sistem Informasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Innovation in Research of Informatics (INNOVATICS) Jutsi: Jurnal Teknologi dan Sistem Informasi Jurnal Teknisi Journal of Computer Science and Informatics Engineering Jurnal Teknik Informatika dan Desain Komunikasi Visual Journal of Innovative and Creativity Jurnal Pengabdian Kepada Masyarakat Indonesia
Yuni Handayani
Universitas Selamat Sri
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Mapping Leading Commodities of Community Forest Plantations Based on Productivity Using the K-Means Clustering Algorithm Taufik Hidayat; Yuni Handayani; Muhammad Khozin; Tri Muji Waluyo; Dian Novitaningrum; Tresi Aprilia; Muchamad Achsin Samas
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1797

Abstract

Kendal Regency has significant potential in community forest plantations, which contribute to the regional economy. However, the mapping of leading commodities based on productivity has not been conducted optimally. This study aims to map leading community forest plantation commodities using the K-Means Clustering algorithm. The novelty of this study lies in the application of the K-Means Clustering algorithm by integrating land area and production volume as the basis for mapping leading commodities at the regency level. Secondary data from the Central Bureau of Statistics of Kendal Regency for the 2019–2023 period, covering seven community forest plantation commodities, were used. The research stages included data preprocessing using Min-Max normalization, clustering into three clusters using the K-Means algorithm, and cluster evaluation employing the Within-Cluster Sum of Squares (WCSS). The results show that the K-Means algorithm successfully grouped the commodities into three clusters based on their productivity characteristics. Sugarcane formed a distinct cluster as the leading commodity due to its highest productivity despite its relatively small cultivation area. These findings provide data-driven insights to support decision-making for the development of community forest plantations in Kendal Regency
Aplikasi Pendaftaran Online Poliklinik Di Rumah Sakit Karomah Holistic Berbasis Android M. Arief Kurniawan; Yuni Handayani; Muhammad Khozin
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3272

Abstract

Karomah Holistic Hospital is a healthcare facility committed to providing high-quality and efficient medical services to the community. To enhance patient convenience in accessing healthcare services, an Android-based outpatient registration application has been developed. This application enables patients to register online, reduce physical queues, and provide more structured information regarding doctor schedules, room availability, and laboratory and radiology test results. The development of this application utilized the Waterfall method, which includes the stages of requirement analysis, design, implementation, testing, and maintenance. The application was developed using the Kotlin programming language with Jetpack Compose as the user interface framework and supported by WorkManager to manage the delivery of appointment reminder notifications. User Acceptance Testing of the application demonstrated that it is easy for patients to use and helps them access healthcare services more quickly and conveniently. With this application, it is hoped that patients will experience more efficient and comfortable service at Karomah Holistic Hospital.Keywords: Clinic registration; Android; Jetpack compose; Workmanager; Waterfall AbstrakRumah Sakit Karomah Holistic adalah fasilitas layanan Kesehatan yang berkomitmen untuk memberikan pelayanan medis yang berkualitas dan efisien kepada masyarakat. Untuk meningkatkan kemudahan pasien dalam mengakses layanan kesehatan, dikembangkan aplikasi pendaftaran poliklinik berbasis Android. Aplikasi ini memungkinkan pasien untuk melakukan pendaftaran secara online, mengurangi antrean fisik, serta memberikan informasi yang lebih terstruktur terkait jadwal dokter, ketersediaan kamar, dan hasil pemeriksaan laboratorium maupun radiologi. Pengembangan aplikasi ini menggunakan metode Waterfall, yang mencakup tahap analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Aplikasi dikembangkan dengan bahasa pemrograman Kotlin menggunakan Jetpack Compose sebagai framework antarmuka pengguna, serta didukung oleh WorkManager untuk mengatur pengiriman notifikasi pengingat jadwal pemeriksaan. Pengujian aplikasi menggunakan metode User Acceptance Test menunjukkan bahwa aplikasi ini mudah digunakan oleh pasien dan membantu mereka dalam mengakses layanan kesehatan secara lebih cepat dan praktis. Dengan adanya aplikasi ini, diharapkan pasien dapat merasakan pengalaman layanan yang lebih efisien dan nyaman di Rumah Sakit Karomah Holistic. 
Liquefied Petroleum Gas (LPG) Leak Detection Mitigation System with MQ-6 Sensor based on the Internet of Things (IoT) Dian Novitaningrum; Yuni Handayani; Taufik Hidayat
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16705

Abstract

The community is beginning to shift from the use of petroleum fuel to Liquefied Petroleum Gas (LPG). In 2023, the Kendal Regency Statistics Agency recorded 53 cases of fire. One of the factors contributing to these fires was gas cylinder leaks, which require preventive measures, education, and mitigation efforts for the proper use of LPG. This research was conducted by designing an LPG gas leak detection system based on the Internet of Things (IoT) using an MQ-6 sensor to notify users of emergencies. The systems aims to notify users via the Blynk application to prevent gas leaks. The research method includes designing the device by assembling and testing components. Additionally, software was developed to connect the sensor to the notification application using Blynk. The system can detect LPG gas leaks within a range of 1-16 cm. A safe threshold is defined as gas levels < 40 ppm, while levels >45 ppm indicate a hazardous status. The conclusions from this research shows that the average gas concentration when the green LED is on 33 ppm with a detection time of 0 seconds, the yellow LED at 40.6 ppm with a detection time of 11.6 seconds, and the red LED at 50 ppm with a detection time of 25.3 seconds, accompanied by a buzzer sounding as a warning of a gas leak in the LPG cylinder. Further research focused on improving the accuracy of the system connected to users WhatsApp accounts.
PERBANDINGAN ALGORITMA LOGISTIC REGRESSION DAN NAÏVE BAYES CLASSIFIER DALAM IDENTIFIKASI PENYAKIT LIVER Yuni Handayani; Taufik Hidayat; Dian Novitaningrum; Abdul Rahman Ismail
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.2892

Abstract

Abstract: Liver disease is a condition caused by various factors that can damage liver function, such as viral infections and alcohol consumption. Additionally, obesity is closely associated with liver damage. Over time, liver damage can lead to serious consequences. The presence of experts in this field is crucial to addressing liver disease by identifying the symptoms experienced by patients, determining the type of liver disease affecting them, and providing appropriate treatment guidance. The severity of this disease in Indonesia is evident from various studies, research, and related observations. In this study, researchers utilized and compared two data mining classification methods, namely Logistic Regression and Naïve Bayes, to diagnose liver disease. The findings revealed that the Logistic Regression method achieved an accuracy rate of 84.62% with an area under the curve (AUC) value of 0.841, while the Naïve Bayes method achieved an accuracy rate of 83.71% with an AUC value of 0.816. Based on the t-test, it was found that there was no significant difference between the two methods, with a p-value of 0.821 > 0.05. This indicates that the performance of Logistic Regression is comparable to Naïve Bayes in diagnosing liver disease.Keyword: Liver Disease, Logistic Regression, Naïve Bayes, Confusion Matrix, ROC CurveAbstrak: Penyakit hati atau liver adalah kondisi yang disebabkan oleh berbagai faktor yang dapat merusak fungsi hati, seperti infeksi virus dan konsumsi alkohol. Selain itu, obesitas juga memiliki kaitan erat dengan kerusakan hati. Dalam jangka panjang, kerusakan hati dapat menimbulkan konsekuensi serius. Kehadiran ahli di bidang ini sangat diperlukan untuk membantu menangani masalah penyakit hati dengan mengidentifikasi gejala yang dialami pasien, menentukan jenis penyakit hati yang diderita, serta memberikan panduan penanganan yang sesuai. Skala permasalahan penyakit ini di Indonesia dapat diamati melalui berbagai studi, penelitian, dan pengamatan yang telah dilakukan. Dalam penelitian ini, peneliti menerapkan serta membandingkan dua metode klasifikasi data mining, yaitu Logistic Regression dan Naïve Bayes, untuk mendeteksi penyakit liver. Hasil penelitian menunjukkan bahwa Logistic Regression memiliki tingkat akurasi sebesar 84,62% dengan nilai area under the curve (AUC) sebesar 0,841, sementara Naïve Bayes mencapai akurasi 83,71% dengan AUC sebesar 0,816. Berdasarkan hasil uji-t, tidak ditemukan perbedaan signifikan antara kedua metode tersebut, dengan nilai p = 0,821 yang lebih besar dari 0,05. Ini menunjukkan bahwa performa Logistic Regression sebanding dengan Naïve Bayes dalam proses diagnosis penyakit liver.Kata kunci: Penyakit Liver, Logistic Regression, Naïve Bayes, Confusion Matrix, ROC Curve
Co-Authors Bagus Aji Pradana Darnis, Rahmi Derry Setiawan Dian Novitaningrum Hanif Arruhama Ismail, Abdul Rahman M. Arief Kurniawan Muchamad Achsin Samas Muhammad Khozin Taufik hidayat To Adn Siroj Tresi Aprilia Tri Muji Waluyo