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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Systematics JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmu Teknik dan Komputer Jurnal Ilmiah Wahana Pendidikan TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Society: Jurnal Pengabdian Masyarakat Innovative: Journal Of Social Science Research
garno, Garno
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Journal : Jurnal Informatika dan Teknik Elektro Terapan

 1 
IMPLEMENTASI ALGORITMA K-MEANS UNTUK KLASTERISASI DALAM PENGELOLAAN PERSEDIAAN OBAT (STUDI KASUS : APOTEK NAZA) Fitriyani, Dede; Jajuli, Mohamad; Garno, Garno
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4921

Abstract

Apotek Naza plays an important role in providing medicines to the community. This study utilizes sales data from Apotek Naza for the period of July to December 2023. The K-Means algorithm is used to cluster the medicine data into clusters representing different sales patterns. The Elbow Method is employed to determine the optimal number of clusters (K) based on the Sum of Square Error (SSE). Evaluation is conducted using the Silhouette Coefficient (SC) to measure the quality of the resulting clusters. The analysis results show that the distribution of medicines in each cluster is as follows: 13.7% or 70 items are classified in the high-usage cluster (Cluster 0 - High), 57.5% or 294 items are classified in the medium-usage cluster (Cluster 1 - Medium), and 28.8% or 147 items are classified in the low-usage cluster (Cluster 2 - Low). This indicates a dominance of medium-usage medicines in the Apotek Naza dataset. The obtained Silhouette Score is 0.520, indicating that the clustering is well performed. According to Table 2.1 on the criteria for measuring clustering based on the Silhouette Coefficient (SC), this score indicates that the resulting clusters are fairly compact and well-separated from each other. Keywords: Medicine Inventory, Data Mining, K-Means, KDD, Elbow Method, Silhouette Coefficient
STUDI KOMPARASI ALGORITMA RANDOM FOREST CLASSIFIER DAN SUPPORT VECTOR MACHINE DALAM PREDIKSI PENYAKIT JANTUNG Alfajr, Nur Halizah; Garno, Garno; Yusup, Dadang
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6569

Abstract

Heart disease is a non-communicable disease with a high mortality rate both globally and in Indonesia. According to WHO, around 17.9 million deaths occur each year due to cardiovascular diseases. Early prediction is crucial to reducing mortality and improving life expectancy. This study compares the performance of machine learning algorithms Random Forest Classifier and Support Vector Machine in predicting heart disease. The dataset consists of 5432 medical records from cardiac outpatients at RSUD Kabupaten Bekasi in 2024, with two classes (labeled 1 (heart disease) = 3068 and labeled 0 (non-heart disease) = 2364). Models were developed using the Knowledge Discovery in Databases (KDD) approach. Evaluation results show that the Support Vector Machine model achieved the best performance compared to Random Forest Classifier with 65% accuracy, 70% precision, 68% recall, and 64% f-measure. Cross-validation and ROC analysis also indicated that Support Vector Machine obtained the highest AUC score, ranging from 0.67 to 0.68, which is categorized as poor classification.
PERANCANGAN SISTEM PEMANTAU DETAK JANTUNG PASIEN MENGGUNAKAN SERVER WEB BERBASIS INTERNET OF THINGS (IoT) PADA KLINIK BIDAN NINING Setiawan, Fikri Maulana; Purwantoro, Purwantoro; Garno, Garno
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6677

Abstract

Dalam konteks pelayanan kesehatan di Indonesia, khususnya pada fasilitas kesehatan tingkat pertama seperti klinik bidan, keterbatasan peralatan medis modern sering menjadi hambatan dalam memberikan layanan kesehatan optimal. Penelitian ini dilatarbelakangi oleh tantangan spesifik di Klinik Bidan Nining yang meliputi ketiadaan alat pemantauan detak jantung, tingginya angka rujukan ke rumah sakit, serta beban ekonomi dan waktu yang ditanggung pasien untuk mendapatkan pemeriksaan lanjutan. Solusi yang diusulkan adalah sistem terintegrasi yang menggabungkan sensor detak jantung MAX30102 dengan mikrokontroler NodeMCU ESP8266 yang terhubung ke web server, memungkinkan pemantauan dan analisis data kesehatan secara real-time.
Co-Authors Aditya Daffa Syahputra Afridho, Afridho Ikhsan Alfajr, Nur Halizah Alfia, Alfia Meilani Putri Alwansyah, Izhari Ananda, Tri Darma Ananta, Ananta Ziaurohman Az Zaki Anisah Apriade Voutama Ari, Ari Rizwan Aries Suharso Arip Solehudin Betha Nurina Sari Chandraditio, Ramadhan Deltania, Dellia Elvis Syahri, Mohamad Fauzan, Muh. Yoga Fitriyani, Dede Hayah, Najmil Heru Widiyanto, Muhammad Heryana, Nono Hidayat, Ibnu Nur Ihya Ullummuddien, Damareindra Iqbal Maulana Irawan, Agung Susilo Yuda Irna Purnahasanah Jamaludin, Asep Jaman, Jajam Haerul Johan Johan Juardi, Didi Kurnia Abdullah, Kunaifi Maheswara, Maheswara Abhista Hamdan Hafiz Maria, Giovanny Mayasari, Rini Mufid, Tsaqif Mu'tashim Muhammad Lias, Lintar Mukholad Fauzi, Wildan Novalia, Elfina Nur Padilah, Tesa Nurina Sari, Betha Prihandani, Kamal Primaya, Aji Puad, Salim Purwantoro Rahma Putri, Annisa Rahmayani, Fenny Ramadhan Harahap, Yudha Ramadhan, Fajar Kurnia Risma, Risma Auliya Salsabilla Riza Ibnu Adam, Riza Ibnu Safitri, Nur Sandra Nathalie Silalahi, Jovanka Setiawan, Fikri Maulana Sofi Defiyanti Sufah, Feila Surya Prabu Al Amin, Sinar Susilo Yuda Irawan, Agung Susilo Yuda Irawan, Agung Umaidah, Yuyun Wildan Bahari, Bayu Yusuf, Dadang Yusup, Dadang Zidane, M Yazid