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All Journal JURNAL SISTEM INFORMASI BISNIS JSAI (Journal Scientific and Applied Informatics) Specta Journal of Technology Jurnal Sibermas (Sinergi Pemberdayaan Masyarakat) Jurnal Ilmu Komputer
Gani, Hilmansyah
Universitas Negeri Gorontalo
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Medicine & Pharmacology Public Health Social Sciences

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Penerapan Metode AHP-TOPSIS Untuk Penyeleksian Permohonan Kredit Pada Koperasi Pegawai Republik Indonesia Gani, Hilmansyah; Suseno, Jatmiko Indro
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 5, No 1 (2015): Volume 5 Nomor 1 Tahun 2015
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.762 KB) | DOI: 10.21456/vol5iss1pp33-39

Abstract

In this study discusses the selection of a decision support system of credit proposals, which make loans to members of the Cooperative Republic of Indonesia ( KPRI ) savings and loan. Decision support system used to aid managers in determining which members are entitled to be given a loan. Errors in granting loans to members of the cooperative will menyebebakan loss or bad credit cooperatives. The method used in this study are TOPSIS ( Technique Order Preference by Similarity to Ideal Solution ) and AHP ( Analytical Hierarchy Process ). TOPSIS is used for ranking members of the cooperative are given a loan, while the AHP is used for the weighting of the criteria used in TOPSIS. The results of this study is an application or software that can menentukkan members who make loans based on lending criteria, large installment, destination, length installment, the basic salary, the last member status, previous loans, and deposits. So that these applications can provide a solution for managers in selecting members to be given credit loans.
Sosialisasi Dan Pelatihan Sistem Informasi Potensi Wisata Pada Desa Botutonuo Muchlis Polin; Manda Rohandi; Mohammad Hidayat Koniyo; Arip Mulyanto; Dian Novian; Salahudin Olii; Moh. Ramdhan Arif Kaluku; Hilmansyah Gani; Nikmasari Pakaya; Abas Kaluku; Rahman Takdir; Indhitya R. Padiku; Edi Setiawan; Alfian Zakaria
Jurnal Sibermas (Sinergi Pemberdayaan Masyarakat) Vol 10, No 2 (2021): Jurnal Sbermas (Sinergi Bersama Masyarakat)
Publisher : Univeristas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/sibermas.v10i2.8306

Abstract

Desa Botutonuo adalah salah satu desa di Kecamatan Kabila Bone Kabupaten Bone Bolango yang memiliki berbagai macam potensi terutama potensi wisata bahari. Namun demikian, jika dinilai dari segi pemanfaatan teknologi informasi, maka potensi ini masih perlu dikelola lebih baik lagi dengan cara memanfaatkan teknologi informasi untuk melakukan publikasi ataupun promosi pariwisata yang ada. Kegiatan pengabdian ini bertujuan untuk memudahkan masyarakat dan pemerintah dalam mengelola dan mempromosikan potensi Desa Botutonuo terutama dalam sektor pariwisata. Selain itu, kegiatan ini juga bertujuan untuk memudahkan aparat desa dalam mengelola administrasi kependudukan melalui implementasi sistem informasi desa. Metode yang digunakan dalam kegiatan ini adalah pendampingan bagi aparat desa maupun masyarakat. Kegiatan pengabdian berlangsung dengan sukses dan mendapatkan apresiasi yang sangat baik dari pemerintah desa dan masyarakat.
PERBANDINGAN ALGORITMA NAÏVE BAYES DAN C4.5 UNTUK KLASIFIKASI BANTUAN RUMAH SEHAT Irawan Ibrahim; Hilmansyah Gani; Rizal Lamusu; Yulan Humolungo
Jurnal Ilmu Komputer (JUIK) Vol 2, No 1 (2022): February 2022
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (822.422 KB) | DOI: 10.31314/juik.v2i1.1477

Abstract

Klasifikasi adalah sebuah teknik untuk menentukan keanggotaan kelompok berdasarkan data-data yang sudah ada. Penentuan pemilihan penerima bantuan rumah sehat hanya di putuskan melalui forum musyawarah desa sehingga membutuhkan ketelitian dalam pemenuhan kriteria dan syarat penerima bantuan rumah sehat. Pada pengumpulan data, data yang didapatkan memiliki 11 kriteria di antaranya yaitu nama kepala keluarga, pekerjaan, usia pernikahan status perkawinan, jumlah anggota keluarga, status kepemilikan tanah, atap, langit-langit, lantai, dinding dan jamban. Tahap pengujian menggunakan 114 record data. Keakurasian dari hasil uji coba menggunakan Algoritma Naïve Bayes dan C4.5 ditinjau dari dua parameter yaitu X-Validation dan jumlah data training. Nilai keakurasian hasil klasifikasi yang ditinjau dari parameter Algoritma C4.5 mendapatkan nilai akurasi yang tertinggi dengan tingkat akurasi 96.51% dibandingkan dengan Algoritma Naïve Bayes dengan tingkat akurasi sebesar 95,61%.
Perbandingan Metode Naïve Bayes dan C4.5 Klasifikasi Status Gizi Bayi Balita Alter Lasarudin; Hilmansyah Gani; Misran Tomayahu
SPECTA Journal of Technology Vol. 6 No. 3 (2022): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (321.744 KB) | DOI: 10.35718/specta.v6i3.789

Abstract

In supporting good services, an orderly, neat and meticulous work procedure are needed. So that information will be produced quickly, precisely and accurately. In health institution, there is a lot of data which increase every year. One of them is data on toddler growth and development. However, the excessive data have consequences more difficult to learn the data were only used as archives. The use of data mining techniques is expected to help in overcoming the imperfent development of toddlers. In this study, researcher compared the classification techniques of performance methods (C4.5) and Naïve Bayes. The attributes used in this research consist of Gender, Age, Weight, Body Length, Region and Growth with 500 data. The results of the research conducted, based on the accuracy and recall values ​​of Naive Bayes were higher than the accuracy value of C4.5 with an accuracy value 94.20% for Naive Bayes, and C4.5 with an accuracy value of 85.80%. Although, in this study the Precision level is higher Naïve Bayes which is 94.20% compared to C4.5 which is 85.80%. The final result of this research is that the Naïve Bayes method is better used than the decision tree method. Keywords: Data Mining, Classification, Naïve Bayes, C4.5
PERANCANGAN PROTOTYPE WEDDING ORGANIZER SYAR’I BERBASIS WEB Hilmansyah Gani; yulanda yunus
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 1 (2024): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i1.6057

Abstract

In the current digital era, there is a growing demand for wedding services that adhere to Sharia or Syar’i principles. This research aims to design a web-based Wedding Organizer prototype that meets Syar’i criteria. The system development methodology involved an initial survey to identify user needs and preferences, followed by the design, development, and evaluation phases of the prototype. The result is a web-based Wedding Organizer prototype that not only simplifies the wedding planning process but also ensures that every aspect aligns with Syar’i principles. Overall, there are 13 interface design prototypes, consisting of the Home Page, Login Page, Customer Registration Data Page, Product Type Data Page, Product Data Page, Package Data Page, Customer Order Data Page, Payment Data Page, Customer Order Execution Page, Customer Data Page, Payment Account Page, Customer Order List Page, and Azariah Wedding Documentation Page. This prototype is expected to be an innovative solution for couples who want to have a wedding in accordance with Islamic teachings.
Co-Authors Abas Kaluku Alter Lasarudin Arip Mulyanto Dian Novian Edi Setiawan Indhitya R Padiku Irawan Ibrahim Manda Rohandi Misran Tomayahu Moh Hidayat Koniyo, Moh Hidayat Moh Ramdhan Arif Kaluku, Moh Ramdhan Arif Muchlis Polin Nikmasari Pakaya Rahman Takdir Rizal Lamusu Salahudin Olii Suseno, Jatmiko Indro Yulan Humolungo yulanda yunus Zakaria, Alfian