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All Journal Syntax Jurnal Informatika Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika dan Teknik Elektro Terapan Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JSiI (Jurnal Sistem Informasi) METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Pelita : Jurnal Penelitian dan Karya Ilmiah JUTIS : Jurnal Teknik Informatika Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) International Journal Mathla'ul Anwar of Halal Issues Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Pada Masyarakat Jurnal Dharmabakti Nagri
Rizky, Robby
Universitas Mathla'ul Anwar
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Implementasi Metode Simple Additive Weighting (SAW) Untuk penentuan Wali Kelas Berdasarkan Prestasi Guru Pada SMAN 6 Pandeglang Rizky, Robby; Susilawati, Susilawati; Setiyowati, Sri; Hakim, Zaenal; Pratama, Aghy Gilar; Yunita, Ayu Mira; Sugiarto, Agung; Wibowo, Andrianto Heri; Susanti, Ervi Nurafliyan; Wardah, Neli Nailul; Sujai, Lili; Prianggita, Veni Agustini; Hakim, Moh Azizi; Sukmara, Sony; Heriyana, Erik
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Permasalahan pada penelitian ini yaitu sulitnya menentukan guru berprestasi yang sesuai dengan kebutuhan sekolah pada saat ini. Tujuan pada penelitian ini membangun analisis terkait pemilihan guru berprestasi untuk menjadi walikelas yang di harapkan. Metode yang digunakan pada penelitian ini menggunakan Simple Additive Weighting (SAW) dengan menentukan kriteria, menentukan nilai setiap alternatif, menentukan bobot kriteria, membuat matriks keputusan dan normalisasi matriks keputusan. Hasil dari penelitian ini berupa analisis sistem yang telah di olah menggunakan metode SAW dengan hasil Hana Nabilah dengan peringkat 1, Figo Hermansyah peringkat 2, Dewi Ayudiah peringkat 3, Bilal mustopa peringkat 4, Maryadi peringkat 5, Intan Lestari peringkat 6, Muhamad Nelson peringkat 7. Maka yang berhak mendapatkan guru terbaik yang menjadi wali kelas yaitu Hana Nabilah dengan peringkat 1.
Machine Learning Untuk Klasifikasi Gizi Balita Menggunakan Algoritma Random Forest Hidayat, Taufik; Kurniawan, Hanif Fajar; Nugrogo, Asep Hardiyanto; Sukisno, Sukisno; Rizky, Robby
InComTech : Jurnal Telekomunikasi dan Komputer Vol 15, No 2 (2025)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v15i2.30517

Abstract

Kesehatan balita merupakan isu kritis dalam pembangunan suatu negara. Penilaian status gizi balita adalah langkah awal untuk mengidentifikasi risiko malnutrisi dan memberikan intervensi yang tepat. Dalam penelitian ini, kami mengusulkan sebuah pendekatan inovatif menggunakan teknik Machine Learning, khususnya algoritma Random Forest, untuk klasifikasi status gizi balita berdasarkan karakteristik demografis dan pola makan. Dataset yang digunakan terdiri dari informasi demografis seperti usia, jenis kelamin, berat badan, tinggi badan, dan data gizi pada setiap balita. Algoritma Random Forest dipilih karena kemampuannya dalam mengatasi overfitting, mengelola data yang tidak seimbang, dan memberikan hasil klasifikasi yang akurat. Berdasarkan penelitian yang telah dilakukan dapat ditarik kesimpulan Tingkat akurasi yang dihasilkan dari algoritma random forest sebesar 83% dari 168 sampel menunjukkan bahwa model klasifikasi yang digunakan memberikan prediksi yang sempurna atau benar untuk seluruh data uji yang digunakan.
Development of the Multi-Channel Clustering Hierarchy Method for Increasing Performance in Wireless Sensor Network Rizky, Robby; Hakim, Zaenal; Setiyowati, Sri; susilawati, Susilawati; Yunita, Ayu Mira
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3348

Abstract

Wireless Sensor Networks are technologies that make it possible to observe phenomena. The problem is data delays in covering the distance from the origin to the destination. Packet Loss is a condition that shows the number of lost packets and the total queue length caused by data processing time. This research aims to develop a cluster-based protocol. This research uses a multichannel hierarchical clustering method and adds odd-even by dividing the network into several channels and forming a cluster head for each channel. The results of this research are Channel 1 with a throughput value of 1.88, channel 2 with a throughput value of 21.68, channel 3 with a throughput value of 1.62, and channel 4 with a throughput value of 42.44. The conclusion of this study is that the throughput results are smaller compared to the Multi-Channel Clustering H ierarchy method, because not all nodes are active
Determining Toddler's Nutritional Status with Machine Learning Classification Analysis Approach Hidayat, Taufik; Ridwan, Mohammad; Iqbal, Muhamad Fajrul; Sukisno, Sukisno; Rizky, Robby; Manongga, William Eric
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4092

Abstract

The nutritional status of toddlers is a common issue many countries face worldwide. Various facts indicate that malnutrition is a primary focus for many researchers. Several efforts have been made to address this problem, including developing analytical models for identification, classification, and prediction. This study aims to evaluate the nutritional status of children by utilizing a classification analysis approach using Machine Learning. This research aims to improve the accuracy of the classification system and facilitate better decision-making in stunted toddlers, which is a priority, especially in the health sector. The Machine Learning classification analysis process will later utilize the performance of the Naive Bayes algorithm, the Support Vector Machine algorithm, and the Multilayer Perceptron algorithm. ML performance can be optimized using gridsearchCV to produce optimal classification analysis patterns. The data set of this study uses 6812 toddler data sourced from the Health Center at the Tangerang Regency Health Office. Based on the research presented, Machine Learning performance in analyzing nutritional status classification provides maximum results. The results are reported based on a precision level with an accuracy of 88%. The results of this analysis can also present a classification of nutritional status based on knowledge. This study can contribute to and update the analysis model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.
Co-Authors Aghi Gilar Pratama Aghy Gilar Pratama aghy gilar pratama Aghy Gilar Pratama Aghy Gilar Pratama Agung Sugiarto Andrianto Heri Wibowo ARDIANSYAH ARDIANSYAH Ayu Mira Ayu Mira Yunita Dedeh Jubaedah Dentik Karyaningsih ervi nuraflian susanti Hadi Susilo, Hadi Hakim, Moh Azizi Hardianto, Asep Heriyana, Erik Hudaya, Dadan Ahmad Imat Maftuhah Indriati, Marlinda Iqbal, Muhamad Fajrul Jayasri, Sandika Kurniawan, Hanif Fajar lili sujai M. Muslim Manongga, William Eric Mohammad Ridwan Mohammad Ridwan Neli Nailul Warda Neli Nailul Wardah Nugrogo, Asep Hardiyanto Nugroho, Asep Hardiyanto Nurafliyan Susanti, Ervi Nuryati Nuryati Pratama, Aghy Gilar Prianggita, Veni Agustini Renji Purnama Alais Rusmala, Rusmala Siddiq, Muhammad Jafar Siti Yumsinah Sri Setiowati Sri Setiyowati Sri Setiyowati Sri Setiyowati Sri Setyowati Suhendi, Aldi Sujai, Lili Sukisno Sukisno Sukisno Sukisno, Sukisno Sukmara, Sony Sumarno . Suryandani, Hendy Susanti, Ervi Nuraflian Susanti, Ervi Nuravlian Susilawati . Susilawati Susilawati Susilawati, Susilawati Susilowati Susilowati Taufik Hidayat Taufik Hidayat Trisnawati, Desi Veny Agustini Prianggita Wibowo, Andrianto Heri Yuniarsih, Eko Zaenal Hakim