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PERBANDINGAN METODE WEIGHTED PRODUCT DAN SIMPLE ADDITIVE WEIGHTING DALAM SELEKSI PENGURUS FORUM ASISTEN (STUDI KASUS : UNIVERSITAS AMIKOM YOGYAKARTA) Pradana, Musthofa Galih; Kusrini, Kusrini; Luthfi, Emha Taufiq
Informasi Interaktif Vol 4, No 2 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (341.697 KB)

Abstract

The assistant forum is an organization built by AMIKOM Yogyakarta University which serves as an official forum for Practicum Assistants. In the Forum Assistant there are daily administrators in charge of managing the daily activities of the organization. In determining the day-to-day management, a selection process is conducted, so that the elected management is the right individual and able to make the Assistant Forum organization better. Because of the importance of the selection process, a system that is able to provide recommendations to determine eligible individuals is needed. administrator. There are many methods that can be used in decision support systems, including the Weighted Product method and Simple Additive Weighting. The data of the candidates for Forum Assistant will be applied to these two methods and a comparison is made of the ranking results of the Weighted Product method and Simple Additive Weighting. The results obtained from this study are ranking from the Weighted Product method and Simple Additive Weighting which produces the same ranking of 3 data from a total of 10 data tested or similarity percentage of 20%.Keywords: Selection, Decision Support System, Weighted Product, Selection.
KOMPARASI METODE NAÏVE BAYES DAN C4.5 DALAM KLASIFIKASI LOYALITAS PELANGGAN TERHADAP LAYANAN PERUSAHAAN Pradana, Musthofa Galih; Saputro, Pujo Hari
Indonesian Journal of Business Intelligence (IJUBI) Vol 3, No 1 (2020): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Program Studi S1 Sistem Informasi Fakultas Komputer Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v3i1.1205

Abstract

Keberadaan pelanggan bagi jalannya sebuah usaha sangatlah penting. Pelanggan memiliki  kecenderungan yakni  untuk tetap lanjut berlangganan dengan perusahaan atau sebaliknya berhenti berlangganan. Salah satu teknik yang dapat digunakan untuk mengidentifikasi kecenderungan loyalitas pelanggan  adalah dengan klasifikasi data. Berdasarkan data pelanggan yang dimiliki perusahaan dapat dilakukan pengolahan data atau data mining dengan mengkelompokan pelanggan yang loyal dan yang tidak loyal. Ada banyak metode yang dapat diterapkan untuk klasifikasi data, diantaranya adalah algortima Naïve Bayes dan C4.5. Kedua metode ini menghasilkan akurasi yang berbeda ketika digunakan untuk proses klasifikasi data. Digunakan 2 skenario dalam proses pengujian kedua algoritma,  skenario membagi data dalam data testing dan training serta skenario pengujian menggunakan cross validation. Hasil kedua skenario ini menunjukan bahwa metode C4.5 lebih unggul dibandingkan dengan metode Naïve Bayes dengan akurasi skenario 1 sebesar 78,6086 % dan skenario 2 akurasi sebesar 78,61%. AbstrakKeberadaan pelanggan bagi jalannya sebuah usaha sangatlah penting. Pelanggan memiliki  kecenderungan yakni  untuk tetap lanjut berlangganan dengan perusahaan atau sebaliknya berhenti berlangganan. Salah satu teknik yang dapat digunakan untuk mengidentifikasi kecenderungan loyalitas pelanggan  adalah dengan klasifikasi data. Berdasarkan data pelanggan yang dimiliki perusahaan dapat dilakukan pengolahan data atau data mining dengan mengkelompokan pelanggan yang loyal dan yang tidak loyal. Ada banyak metode yang dapat diterapkan untuk klasifikasi data, diantaranya adalah algortima Naïve Bayes dan C4.5. Kedua metode ini menghasilkan akurasi yang berbeda ketika digunakan untuk proses klasifikasi data. Digunakan 2 skenario dalam proses pengujian kedua algoritma,  skenario membagi data dalam data testing dan training serta skenario pengujian menggunakan cross validation. Hasil kedua skenario ini menunjukan bahwa metode C4.5 lebih unggul dibandingkan dengan metode Naïve Bayes dengan akurasi skenario 1 sebesar 78,6086 % dan skenario 2 akurasi sebesar 78,61%.
ANALISIS TINGKAT KEMATANGAN LAYANAN JARINGAN BERDASARKAN PERSPEKTIF INTERNAL MENGGUNAKAN COBIT 4.1 PADA UNIVERSITAS KRISTEN IMMANUEL YOGYAKARTA Azriel Christian Nurcahyo; Musthofa Galih Pradana; Rifqi Hammad
Management and Sustainable Development Journal Vol 2 No 1 (2020): Management and Sustainable Development Journal
Publisher : Department of Management - Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/msdj.v2i1.156

Abstract

Predikat akreditasi “B” pada Universitas yang diperoleh kampus UKRIM pada tahun 2018 membuat pihak manajerial PUSKOM melakukan evaluasi dengan mentitikberatkan sektor peningkatan pengelolaan dan penggunaan teknologi informasi dari tahun ke tahun terutama di bidang infrastruktur layanan bandwidth jaringan internet serta layanan sistem informasi akademik. Kampus UKRIM telah menerapkan teknlogi informasi dalam mendukung proses bisnis dengan tujuan agar pihak manajerial mampu memahami sejauh mana teknologi informasi mampu berperan untuk mendukung proses bisnis guna mencapai tujuan organisasi, maka perlu dilakukan evaluasi. Evaluasi teknologi informasi merupakan rangkaian proses pengumpulan data dari semua kegiatan informasi yang hendak dievaluasi dan diawasi terhadap teknologi informasi perusahaan tersebut untuk mencapai tujuannya. COBIT (Control Objectives for Information and Related Technology) merupakan standar kerangka kerja baku internasional yang digunakan untuk melakukan audit tingkat kematangan tata kelola proses penyelenggaraan dalam pengelolaan suatu organisasi. Tingkat kematangan atau maturity level pada Cobit terdiri dari 6 tingkat kematangan yaitu tingkat 0 (non-existent), tingkat 1(initial), 2(repeateable), 3(defined) ,4(managed) dan terakhir tingkat 5 (optimised). Setelah dilakukan analisis maka diperoleh hasil temuan yang menunjukkan tingkat kematangan penerpan teknologi informasi. Berdasarkan hasil yang telah didapatkan maka diketahui bahwa nilai tingkat kematangan paling rendah pada AI3 yaitu 2,34 dan nilai tertinggi pada DS 7 yaitu 3,80. Kata kunci: bandwidth, COBIT (Control Objectives for Information and Related Technology)
Steganography Technique and Modification of Substitution Cipher Using ASCII Code and Fibonacci Sequences Pradana, Musthofa Galih; Saputro, Pujo Hari; Pamekas, Bondan Wahyu
CSRID (Computer Science Research and Its Development Journal) Vol 13, No 2 (2021): CSRID JUNI 2021
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.13.2.2021.77-86

Abstract

Cryptography is widely used to secure data and information so that it is not easily misused by parties who are not interested in the data. One type of cryptographic algorithm is Caesar Cipher and Vigenere Cipher. Both of these algorithms are classic cryptographic algorithms that need to be modified so that they become more optimal in the data security process. The first modification is to modify the Vigenere key using Fibonacci. In general, Vigenere Cipher will repeat the same keyword to complete the number of characters that are lacking so that the number of characters is the same as the number of characters in the plaintext. The second modification is to change or convert plaintext letters into ASCII letters so that the code is more difficult to solve. After the ASCII conversion process is done, the next results will be converted back in Hexa letters. In addition to the two modifications made, the steganography technique is also added by hiding the code behind the media in the form of images. Images that are sampled will be renamed and stored in different places.
Perancangan Sistem Pakar Untuk Mendiagnosa Penyakit Diabetes Mellitus Menggunakan Metode Certainty Factor Design Expert System for Diagnosing Diabetes Mellitus Using Certainty Factor Method Musthofa Galih Pradana; Bondan Wahyu Pamekas; Kusrini Kusrini
CCIT Journal Vol 11 No 2 (2018): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (425.849 KB) | DOI: 10.33050/ccit.v11i2.586

Abstract

Diabetes mellitus is a chronic metabolic disorder caused by the pancreas that does not produce enough insulin, so the body works to be disturbed. But by knowing the symptoms that exist, prevention of diabetes mellitus disease can be done as early as possible with the help of expert systems.One method of expert system used to diagnose symptoms of Diabetes Mellitus is Certainty Factor. The process undertaken in this research starts from literature studies, system design, system implementation and the last is testing the system. In the system design process is done by designing the database required by the expert system and also design the system interface design. After the design process is done then the next step is to implement the design into an expert system application. By using this method, the system gives results of possible symptoms experienced, presentation of beliefs, and treatment solutions based on the facts and the value of confidence given by users in filling out questions that have been given by the system.The results of this system are used to help medical personnel and patients in order to identify the symptoms of diabetes mellitus
Analisis Variable yang Memengaruhi Minat Pemilihan Perguruan Tinggi Musthofa Galih Pradana; Azriel Christian Nurcahyo; Fandli Supandi
Creative Information Technology Journal Vol 7, No 1 (2020): Januari - Juni
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/citec.2020v7i1.202

Abstract

Kebijakan Promosi dari Universitas untuk mendapatkan mahasiswa baru merupakan hal yang vital bagi kelancaran proses penerimaan Mahasiswa Baru. Pihak lembaga perlu mengkaji dan melakukan analisis yang baik sebelum melakukan promosi. Di era sekarang, banyak media yang dapat dijadikan sebagai media promosi. Dengan berkembangnya cara penyebaran informasi serta banyaknya media pihak yang berkepentingan sudah seharusnya jeli mana yang mendapat prioritas lebih dari semua banyak lini media tempat promosi. Ketika tingkat prioritas sudah ditentukan maka upaya promosi akan menjadi lebih mudah dan lebih tepat sasaran. Akan tetapi penentuan tingkat prioritas tidak dapat dilakukan secara asal dan sembarangan. Perlu dilakukan pengkajian yang lebih dalam bukan hanya sekedar intuisi belaka. Penelitian ini membahas tentang pengaruh promosi dengan minat calon mahasiswa memilih AMIKOM sebagai universitas pilihan dengan acuan tiga variable yaitu sosial media, website, dan referensi alumni. Pengolahan data menggunakan software SPSS dan SPSS AMOS untuk mencari validitas, reliabilitas dan uji hipotesis. Hasil yang didapatkan adalah semua variable signifikan, dengan urutan dari nilai tertinggi ke terendah adalah Sosial Media, Referensi Alumni, dan Website. Kata Kunci—Promosi, Validitas, Reliabilitas, HipotesisPromotion policy from the University to get new students is vital for the smooth process of admission of New Students. The institution needs to review and conduct a good analysis before conducting a promotion. In this era, many media can be used as promotional media. With the development of ways of disseminating information and the many media interested parties it should be observant which gets priority over all the many lines of media wherepromotion. When the priority level has been determined, the promotion effort will be easier and more targeted. However, the determination of priority levels cannot be done arbitrarily and carelessly. Deeper studies need to be done not just mere intuition. This study discusses the effect of promotion with the interest of prospective students choosing AMIKOM as the university of choice with three variables reference, namely social media, website, and alumni reference. Data processing using SPSS and SPSS AMOS software to find validity, reliability, and hypothesis testing. The results obtained are all significant variables, with the order from highest to lowest values are Social Media, Alumni Reference, and Website.Keywords— Promotion, Validity, Reliability, Hypothesis
Pengembangan Sistem DAPODIKDAS pada Optimalisasi Pencarian Data Siswa Berprestasi Dema Mathias Lumban Tobing; Yulianto Mustaqim; Musthofa Galih Pradana; Azriel Christian Nurcahyo; Yusuf Hendra Pratama
Creative Information Technology Journal Vol 5, No 4 (2018): Agustus - Oktober
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (725.597 KB) | DOI: 10.24076/citec.2018v5i4.131

Abstract

Dapodikdas serentak diterapkan pada seluruh Sekolah Dasar sejak tahun 2015, dimana banyak kemudahan yang didapatkan apabila sekolah menggunakan aplikasi tersebut seperti pemberian NISN, BOS, BSM, KIP, tunjangan dan sertifikasi guru, pendataan peserta Ujian Nasional, dan pendataan orang tua siswa. Hingga saat ini Dapodikdas belum mengakomodir kebutuhan Kemendikbud dan Sekolah dalam pencarian data siswa berprestasi. Hal ini disebabkan karena belum tersedianya fitur pencarian data siswa berprestasi sebagai bagian optimalisasi penggunaan data. Mengingat siswa berprestasi layak mendapatkan penghargaan oleh negara sesuai UU No 20 Tahun 2003 tentang Sistem Pendidikan Nasional.Pada penelitian ini dilakukan optimalisasi sistem Dapodikdas melalui penambahan fitur pencarian kepakaran data siswa berprestasi. Hasil dari penelitian ini mampu mengoptimalisasikan berupa simulasi Dapodikdas dalam pencarian siswa berprestasi. Perbandingan kecepatan akses query sedikit lebih lambat dibandingkan sistem dapodikdas saat ini namun adanya penambahan fitur pencarian kepakaran siswa mampu memenuhi kebutuhan UU No 20 Tahun 2003. Dari hasil perbandingan dilakukan uji query pencarian biodata siswa lengkap dan orang tua diperoleh Dapodikdas lebih cepat 0.00695 detik dibandingkan Dapodikdas versi optimalisasi 0.007195 detik, akan tetapi terdapat beberapa fitur kelebihan dari versi optimalisasi yaitu pencarian pembinaan siswa, seleksi siswa, dan penghargaan siswa. Penambahan fitur ini diharapkan menjadi sarana Kemendikbud dan Sekolah dalam pencarian bakat siswa berprestasi.Kata Kunci —Dapodikdas, Kemendikbud, Optimalisasi, Kepakaran.Dapodikdas is simultaneously applied to all elementary schools since 2015, where many of the conveniences obtained schools use such applications as NISN, BOS, KIP, teacher’s allowances and certification, National Examination, and parents. Until now, Dapodikdas not accommodate the needs of Kemendikbud and School to search of student data achievement. This is due to unavailability of data search feature of student achievement as part of data usage optimization. In this research, Dapodikdas system optimization is done through the addition of search feature of student data achievement. The result of this research is able to optimize in the form of Dapodikdas simulation in search of achievement students. The comparison of query access speed is slightly slower than the current system but the addition of search features is able to meet the needs of UU No.20 of 2003. The comparison result, it is done by query search of complete student biographical data and parents get faster 0.00695 seconds than Dapodikdas optimization version 0.007195 second, but there are some advantages feature of optimization version that is searching student coaching, selection, and awards. The addition of this feature is expected to be a means of Kemendikbud and School in talent search for outstanding students.Keywords— Dapodikdas, Kemendikbud, Optimalitation, Expertise.
Penerapan Metode K-Means Klustering Untuk Menentukan Kepuasan Pelanggan Musthofa Galih Pradana; Azriel Christian Nurcahyo; Pujo Hari Saputro
Creative Information Technology Journal Vol 7, No 1 (2020): Januari - Juni
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/citec.2020v7i1.185

Abstract

Pengolahan data dapat dilakukan dengan banyak cara dan teknik. Peran data saat ini menjadi sangat penting bagi sebuah perusahaan atau penyedia layanan untuk pelanggan. Pentingnya data saat ini menjadikan proses pengolahan data dilakukan secara mandiri menggunakan metode-metode data mining yang ada. Beberapa metode yang dapat diterapkan diantaranya klasifikasi, prediksi maupun klustering. Masing-masing teknik tersebut memiliki hasil yang dapat dijadikan acuan evaluasi dan perencanaan yang lebih baik lagi. Penelitian ini menerapkan teknik klustering yaitu memisahkan dan mengelompokan data berdasarkan kluster. Dalam klustering ada banyak algortima atau metode yang dapat diterapkan, salah satunya adalah K-Means Klustering. Algoritma K-Means merupakan algoritma yang banyak digunakan untuk mengelompokan data. Hasil dari penelitian ini terbagi menjadi 2 kluster yaitu Kluster 0 yaitu puas dan Kluster 1 yaitu tidak puas ataupun netral. Pengelompokan kluster tersebut berdasarkan dataset yang dimiliki dimana responden mengisi data dan menghasilkan 2 jenis kluster tersebut. Adapun hasil dari proses klustering adalah sebanyak 1303 data masuk kategori kluster 0 atau sebesar 65% dan 697 data masuk kategori kluster 1 atau sebesar 35%. Kata Kunci— Data Mining, Klustering, K-MeansData processing can be done in many ways and techniques. The role of data is now very important for a company or service provider for customers. The importance of data now makes data processing carried out independently using existing data mining methods. Some methods that can be applied include classification, prediction and clustering. Each of these techniques has results that can be used as a reference for evaluation and better planning. This study applies clustering techniques, namely separating and grouping data based on clusters. In clustering there are many algorithms or methods that can be applied, one of which is K-Means Klustering. K-Means algorithm is an algorithm that is widely used to group data. The results of this study are divided into 2 clusters, namely Cluster 0, which is satisfied and Cluster 1, which is not satisfied or neutral. Clustering is based on a dataset that is owned by where the respondent fills in data and produces 2 types of clusters. The results of the clustering process are as many as 1303 data in the category of cluster 0 or 65% and 697 data in the category of cluster 1 or 35%. Keywords— Data Mining, Clustering, K-Means
Maximizing Strategy Improvement in Mall Customer Segmentation using K-means Clustering Musthofa Galih Pradana; Hoang Thi Ha
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.18

Abstract

The application of customer segmentation is very vital in the world of marketing, a manager in determining a marketing strategy, knowing the target customer is a must, otherwise it will potentially waste resources to pursue the wrong target. Customer segmentation aims to create a relationship with the most profitable customers by designing the most appropriate marketing strategy. Many statistical techniques have been applied to segment the market but very large data are very influential in reducing their effectiveness. The aim of clustering is to optimize the experimental similarity within the cluster and to maximize the dissimilarity in between clusters. In this study, we use K-means clustering as the basis for the segmentation that will be carried out, and of course, there are additional models that will be used to support the research results. As a result, we have succeeded in dividing the customer into 5 clusters based on the relationship between annual income and their spending score, and it has been concluded that customers who have high-income levels & have a high spending score are also very appropriate targets for implementing market strategies.
Implementation of Data Mining Using C4.5 Algorithm for Predicting Customer Loyalty of PT. Pegadaian (Persero) Pati Area Office Ridlo Muttaqien; Musthofa Galih Pradana; Andri Pramuntadi
International Journal of Computer and Information System (IJCIS) Vol 2, No 3 (2021): IJCIS : Vol 2 - Issue 3 - 2021
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v2i3.36

Abstract

PT Pegadaian (Persero) is engaged in the business of providing credit services with pawn, non-pawning and gold investment products. One of the right marketing strategies to survive today's high competition is to maintain customer loyalty, researchers use several data variables available in the MIS (Management Information System) in the form of customer transaction frequency, how many products are taken by customers, customer satisfaction and direct interviews. to predict customer loyalty of PT Pegadaian (Persero) by implementing the c4.5 algorithm. The c4.5 algorithm is the algorithm used to create a decision tree. Decision trees are a very powerful and well-known method of classification and prediction. The decision tree method converts very large facts into a decision tree that represents the rule. Rules can be easily understood in natural language. This study aims to determine the accuracy of the C4.5 algorithm to predict customer loyalty of PT Pegadaian (Persero) and the most influential factors in loyalty. The results of the experimental application of the c4.5 algorithm show that the level of accuracy generated in predicting customer loyalty is quite high, namely 89.94% in data testing 1 and 94% in data testing 2. The application of the c4.5 algorithm in predicting customer loyalty of PT Pegadaian (Persero) can be well applied.