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All Journal JUITA : Jurnal Informatika CESS (Journal of Computer Engineering, System and Science) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri ILKOM Jurnal Ilmiah MULTINETICS Simpatik: Jurnal sistem Informasi dan Informatika J-Intech (Journal of Information and Technology) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Ladang Artikel Ilmu Komputer
Satia Suhada, Satia
Program Studi Sistem Informasi, Universitas Bina Sarana Informatika, Sukabumi, Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Jurnal Pilar Nusa Mandiri

 1 
PENGAMBILAN KEPUTUSAN PENERIMA BEASISWA SMA MENGGUNAKAN FUZZY MULTIPLE ATRIBUTE DECISION MAKING MODEL WEIGHTED PRODUCT Taufik Hidayatulloh; Satia Suhada; Eva Nursyifa; Lestari Yusuf
Jurnal Pilar Nusa Mandiri Vol 14 No 2 (2018): PILAR Periode September 2018
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (846.883 KB) | DOI: 10.33480/pilar.v14i2.75

Abstract

Beasiswa merupakan dana yang dapat diberikan oleh pemerintah, perusahaan swasta, kedutaan, universitas atau dari kantor tempat bekerja untuk meningkatkan kualitas sumber daya manusia melalui pendidikan. SMAN 1 Cicurug Sukabumi adalah salah satu instansi pendidikan yang menyelenggarakan beasiswa bagi siswa berprestasi yang akan melanjutkan sekolah di SMAN 1 Cicurug Sukabumi. Terdapat lima kriteria untuk menentukan penerima beasiswa tersebut yaitu: nilai rata-rata raport, nilai rata-rata ijazah, jumlah tanggungan orang tua, penghasilan orang tua dan bidang keahlian dengan bobot-bobot pada setiap kriteria. Untuk mempermudah para pengurus institusi pendidikan dalam menentukan peserta didik baru yang berhak menerima beasiswa, maka perlu adanya suatu sistem rekomendasi keputusan yang berfungsi untuk membantu melakukan seleksi kepada para calon penerima beasiswa. Pada penelitian ini menerapkan Fuzzy Multiple Attribute Decision Making dengan model weighted product sebagai sistem alternatif yang dapat digunakan untuk menentukan penerima beasiswa berdasarkan kriteria yang telah ditentukan, dengan hasil yang diperoleh dari 5 Alternatif yangdigunakan adalah yang memiliki nilai tertinggi pada perankingan dengan nilai vektor S=0.70 dan nilai Vektor V= 0.24.
FEASIBILITY TEST OF POOR RICE RECIPIENTS IN BENCOY SUKABUMI VILLAGE USING NAIVE BAYES Taufik Hidayatulloh; Ardi Winardi; Lestari Yusuf; Satia Suhada; Saeful Bahri
Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Publishing Period for March 2021
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i1.2227

Abstract

A regional head must have a work plan every regional head must have a work plan which is sure to be of benefit to the community. Assisting is a definite work plan in every region. A lot of assistance is usually given from the government to the community and must be managed by the village government so that the aid gets to the right hands. And to improve food security, the people in each region have activities to distribute Poor Rice as a subsidy from the government. In the distribution method, sometimes there are constraints in data collection so that poor rice or what we usually call Raskin is not suitable for distribution. Because of this, a way is needed so that the distribution is appropriate or not in the community in accepting the Raskin so that government assistance can be delivered properly and on target. By using secondary data obtained from Bencoy Village, 205 data were obtained containing the attributes of the eligibility category of Raskin recipients, and 6 categories of attributes were found with the classification method of the Naïve Bayes algorithm. The accuracy value obtained is 96.59%, proving that the prediction using the Naive Bayes algorithm has a good performance. The next results obtained are in the form of AUC value which after being calculated produces a value of 0.999 and this results in an application which is an implementation with a flow that is adjusted to the calculation algorithm in the form of a web-based application.
Co-Authors A.Gunawan Abidin Abidin Acep Suherman Ami Rahmawati Andi Riyanto Ardi Winardi Arif Purnama Daniel Ratag Dasya Arif Firmansyah Dede Wintana Dede Wintana Desi Susilawati Dicky Prayudi Dwiza Riana Eva Marsusanti Eva Marsusanti Eva Nursyifa Gunawan Gunawan Gunawan Gunawan Gunawan Hamdun Sulaiman Jamal Maulana Hudin, Jamal Maulana Jasisca Marleftan Lestari Yusuf Muhamad Reza Vahlefi Purwaningtias, Deasy Rahmawati, Ami Ramdhan Saepul Rohman Rina Riniawati Risdiansyah, Deni Rusda Wajhillah Rusli Nugraha Rusli Nugraha Saeful Bahri Saeful Bahri Selvia Septi Palupi Setyo Bagus Nugraha Siskawati Siskawati Siti Fatimah Susilawati Susilawati Taufik Hidayatulloh Taufik Hidayatulloh Wahyu Nugraha Wintana, Dede Yahya Iskandar Yulhan Wahyudin