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All Journal TEKNIK INFORMATIKA WARTA E-Dimas: Jurnal Pengabdian kepada Masyarakat Fountain of Informatics Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) Network Engineering Research Operation [NERO] KOMPUTEK Jurnal Manajemen Informatika JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Jurnal Tecnoscienza Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Aplikasi Bisnis dan Komputer Literasi: Jurnal Pengabdian Masyarakat dan Inovasi KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Indah Puji Astuti
Universitas Muhammadiyah Ponorogo
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Fountain of Informatics Journal

 1 
Prediksi Ketepatan Waktu Kelulusan Dengan Algoritma Data Mining C4.5 Indah Puji Astuti
Fountain of Informatics Journal Vol 2, No 2 (2017): November
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v2i2.1067

Abstract

The student is one of entities in University or Higher Education. The student has a variety of data, such as self-identity information such as address, type of school, work of parents, type of class, etc. In fact many students whose graduation rate is different, on time and not on time. The number of students who graduate is not on time will be a problem not only for university but also for faculty. The number of students graduating each year is one of points of assessment when faculty or study program submits accreditation. C4.5 Algorithm is one of classification algorithm with decision trees. In this study conducted an analysis of student data Engineering Studies Program University of Muhammadiyah Ponorogo 2012/2013. The decision trees in this case is useful for exploring student data, finding the hidden relationship between a number of candidate input attributes with a target attribute. The input attribute consist of, the type of school, address, work of parent, and type of class. The output attribute to classify is status, which consists of "on time" and "not on time". The results from this analysis shown that in this case the C4.5 algorithm can predict with an accuracy value only 82%.
Sistem Pendukung Keputusan Rekomendasi Reksa Dana Obligasi Menggunakan Metode SAW (Simple Additive Weighting) Ghulam Asrofi Buntoro; Indah Puji Astuti; Riki Widiantoro
Fountain of Informatics Journal Vol 7, No 2 (2022): November 2022
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v7i2.8339

Abstract

AbstrakReksa dana merupakan salah satu alternatif investasi bagi masyarakat yang memiliki keterbatasan modal, waktu dan pengelolaan keuangan dikarenakan dana masyarakat yang telah dikumpulkan dalam reksa dana dikelola oleh manajer investasi yang profesional. Untuk jenis reksa dana yang memiliki resiko sedang atau moderat adalah jenis reksa dana obligasi. Salah satu tempat untuk membeli dan menjual reksa dana secara online adalah aplikasi Bibit. Pada aplikasi bibit per April 2022 terdapat 32 produk reksa dana obligasi. Dari banyaknya produk reksa dana obligasi yang ada masyarakat atau investor kesulitan dalam memilih produk reksa dana obligasi yang terbaik. Maka dari itu, untuk memilih produk reksa dana obligasi terbaik diperlukan sistem pendukung keputusan berbasis web menggunakan algoritma Simple Additive Weighting (SAW). SAW dipilih karena dapat menentukan nilai bobot untuk setiap atribut yang kemudian dilanjutkan dengan proses perangkingan dalam menyeleksi alternatif terbaik. Pengujian sistem dilakukan menggunakan metode white box, hasilnya semua perintah terhadap sistem berhasil dilakukan dengan valid. Hasil dari penelitian ini berupa aplikasi sistem pendukung keputusan berbasis web yang responsive dan dapat memberikan rekomendasi untuk reksa dana obligasi terbaik dengan sistem perangkingan.Kata kunci: Bibit, Investasi, Reksa Dana, Simple Additive Weighting AbstractMutual funds are one of the alternative investments for people who have limited capital, time, and financial management because the public funds that have been collected in mutual funds are managed by professional investment managers. The type of mutual fund that has moderate or moderate risk, it is a type of bond mutual fund. One of the places to buy and sell mutual funds online is the Bibit application. In the Bibit application as of April 2022, there are 32 bond mutual fund products. Of the many bond mutual fund products that exist, the public or investors have difficulty choosing the best bond mutual fund product. Therefore, to choose the best bond mutual fund product, a web-based decision support system is needed using the Simple Additive Weighting (SAW) algorithm. SAW was chosen because it can determine the weight value for each attribute which is then continued with the ranking process in selecting the best alternative. The results of this study are in the form of a web-based decision support system application that is responsive and can provide recommendations for the best bond mutual funds with a ranking system.Keywords: Bibit, Investments, Mutual Funds, Simple Additive Weighting 
Co-Authors Agung Nur Fachdillah Andy Triyanto Aslan Alwi Bellani Patnandi Cobantoro, Adi Fajaryanto Dewangga Febrianus Rosidin Diah Ervin Arlindila Dimas Aditya Dimas Nando Putro Widodo Dwiyono Ariyadi Dyah Mustikasari Ega Feri Romawati Fauzan Masykur Fredy Setyo Budi Utomo Ghulam Asrofi Buntoro Habiby, Jawwad Sulthon Hardi Prasetiyo Ida Widaningrum, Ida Khoiru Nur Fitri Khoiru Nurfitri Moh Bhanu Setyawan Muh Ardiansyah Prasetyo, Angga Reza Risky Khamdani Riki Widiantoro Tomi Wingga Pratama Viky Friandika Utamandani Wahyu Setyobudi Yusika Destiana