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All Journal Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Information System for Educators and Professionals : Journal of Information System Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jikom: Jurnal Informatika dan Komputer
Pelsri Ramadar Noor Saputra
Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Pengelompokan Data Puskesmas Banyuwangi Dalam Pemberian Imunisasi Menggunakan Metode K-Means Clustering Ahmad Chusyairi; Pelsri Ramadar Noor Saputra
Telematika Vol 12, No 2: Agustus (2019)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (520.929 KB) | DOI: 10.35671/telematika.v12i2.848

Abstract

Peningkatan pelayanan dan penyuluhan layanan imunisasi untuk ibu, bayi dan balita di Puskesmas dalam mencapai target cakupan Imunisasi Dasar Lengkap (IDL). Kabupaten Banyuwangi memiliki 45 Puskesmas yang akan dikelompokkan menjadi 3 kategori, yaitu: Puskesmas mencapai target IDL dengan status cukup, Puskesmas mencapai target IDL dengan status kurang, dan Puskesmas mencapai target IDL dengan status sangat baik. Tujuan penelitian ini adalah untuk mengetahui Puskesmas dalam mencapai target cakupan IDL di Kabupaten Banyuwangi. Metode yang digunakan dalam penelitian ini adalah K-Means Clustering, dimana metode ini dapat mencari partisi yang maksimal dengan prosedur iterasi yang optimal dalam mengelompokkan data secara tepat, dan memiliki ketelitian yang akurat terhadap ukuran objek, sehingga lebih terukur dan efisien dalam pengolahan data yang besar. Kesimpulan dalam penelitian ini adalah cluster pertama memiliki 19 data puskesmas dengan target imunisasi cukup, cluster kedua memiliki 24 data puskesmas dengan target imunisasi kurang, dan cluster ketiga memiliki 2 data puskesmas dengan target imunisasi sangat baik, sehingga Dinas Kesehatan dapat memberikan tugas tambahan bagi kelompok Puskesmas yang memiliki target IDL dengan status kurang untuk mengurangi angka penyakit-penyakit yang dapat dicegah dengan imunisasi (PD3I). Improvement of immunization services and counselling services for mothers, infants, and toddlers in health care centres in achieving the target of Complete Basic Immunization (IDL). Banyuwangi Regency has 45 Puskesmas which will be grouped into 3 categories, namely: Puskesmas achieving IDL targets with sufficient status, Puskesmas achieving IDL targets with insufficient status, and Puskesmas achieving IDL targets with very good status. The purpose of this study was to determine the health centre in achieving the target of IDL coverage in the Banyuwangi Regency. The method used in this research is K-Means Clustering. This method will seek a maximal partition with optimum iteration procedure and has the best precision of the object measurement, so it is more scalable and efficient in processing a large data. The conclusion in this study is the first cluster has 19 health care centers data with sufficient immunization targets, the second cluster has 24 health care centers data with fewer immunization targets, and the third cluster has 2 health care centers data with very good immunization targets, so the Health Office can provide additional tasks for the Puskesmas group who have IDL targets with insufficient status to reduce the number of diseases that can be prevented by immunization (PD3I).
Comparison of Clustering Methods in Grouping Puskesmas Data on Complete Basic Immunization Coverage Pelsri Ramadar Noor Saputra; Ahmad Chusyairi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 6 (2020): Desember 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (785.837 KB) | DOI: 10.29207/resti.v4i6.2556

Abstract

The coverage of Health Care Center toward UCI (Universal Child Immunization) at Banyuwangi Regency in 2018 met the target 91%. Onfortunately with a high amount of immunization, the number of infant deaths reached 138 infants. Total number increased 111 from the previous year. A review of the complete basic immunization data needs to be done. In this research, a clustering method was proposed by comparing the K-Means and Fuzzy C-Means (FCM) algorithm in grouping Health Care Center data. Silhouette Coefficient and Standart Deviation were used to evaluate clusters that were perfomed to find out the accuracy in grouping data. The result showed that the FCM algorithm was better than K-Means based on Silhouette Coefficient results that were close to good, and the calculation of Standart Deviation had a smaller result that was 0.0918 than K-Means with the results of 0.0942. The Grouping of Heath Care Center data can be considered by the Health Department of Banyuwangi Regency in evaluating complete basic immunization services, especially in groups with poor immunization services to reduce infant and child mortality, so a disease that can be prevented with immunization become lower.
COMPARISON OF DISTRIBUTED DATA MINING FOR SELECTION OF THE PROPER MAJORS Pelsri Ramadar Noor Saputra; Hadiq Hadiq
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2017: SNTIKI 9
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (615.172 KB)

Abstract

The majors in STIKOM PGRI Banyuwangi are Artificial Intellegence, Software Engineering and Networking. The students have a different ability on IQ and talent, so the student must choose the majors according to their ability in the field of interest. Grades start from semester 1 until the semester 4 constitute basic ability to be a consideration in determining the right majors. To overcome this problem, this research uses classification technique, which is comparing several algorithms among others C4.5, Naïve Bayes, KNN, Random Forest, and SVM. This algorithm applies to build classification selection of the proper majors. Pairwise T-Test determine as an accuracy indicator to evaluate the performance of classifiers. Results showed that C4.5 seemed to be the best of five classifiers which had highest prediction result. C4.5 was used to generate data which can be used to classifying student majors in STIKOM PGRI Banyuwangi. And the results of the accuracy of other methods close to the results of the method C4.5.
Sistem Pakar Psikologi Perkembangan Anak Menggunakan Algoritma Cosine Pelsri Ramadar Noor Saputra
Jurnal Informatika dan Komputer Vol 13 No 1 (2023): April
Publisher : Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55794/jikom.v13i1.97

Abstract

An expert system is a system that is used to find a solution to an existing problem by using the knowledge base that is stored in the system. Expert systems are not used to replace existing experts, but rather aim to help the work of experts. With this system, ordinary people can solve certain problems from simple to complex without the need for experts in that field. As for experts, this system can be used as an experienced assistant. The expert application that is being developed in this task aims to detect the psychology of child development based on the symptoms that are observed. This application uses the Cosine Algorithm as a calculation formulation. All symptoms will be displayed, and those selected will be automatically processed using the cosine algorithm to obtain disease results. When the symptoms are selected again, the data will be recalculated from the beginning based on the selected symptoms. Based on the results of the trials that have been conducted, it has been shown that while the calculation using the cosine algorithm is somewhat complex, the accuracy of the cosine calculation is high in determining the child's psychological condition based on the selected symptoms.
PEMANFAATAN NEXTJS DAN MONGODB DALAM SISTEM INFORMASI WEB MANAJEMEN DATA BERAS PADA UD SRI UTAMI Munif Sanjaya; Pelsri Ramadar Noor Saputra
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 8 No 1 (2023): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Agustus 2023) - Edisi Khusus
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v8i1.2414

Abstract

Beras termasuk bahan makanan pokok bagi masyarakat Indonesia. Saat ini banyak pabrik atau perusahaan yang menjual beras dalam bentuk merk atau bentuk bahan, termasuk pada penggilingan padi. Salah satu perusahaan penggilingan padi yaitu UD Sri Utami yang bergerak di bidang pemasaran beras. Dalam manajemen data perusahaan serta pemasarannya selama ini masih menggunakan cara manual. UD. Sri Utami ingin merubah model pemasarannya dengan sistem penjualan berbasis Website dengan menggunakan metode Waterfall. Sistem dibangun menggunakan JavaScript, framework NextJS, dan database MongoDB. Pemanfaatan NextJS karena memiliki konsep Server-Side Rendering dan MongoDB berbasis dokumen (JSON) berbasis Cloud. Berdasarkan perancangan sistem, pengembangan sistem berjalan dengan baik yang dibuktikan dari hasil pengujian Blackbox Testing. Dengan adanya pengembangan sistem ini maka dapat membantu UD. Sri Utami dalam manajemen data, termasuk dalam hal pemasaran dan penjualan beras
Co-Authors Chusyairi, Ahmad Hadiq Hadiq Munif Sanjaya Sulaibatul Aslamiyah