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All Journal Katalogis Jurnal Teknologi Informasi dan Ilmu Komputer
Risnandar, Risnandar
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Computer Science & IT Engineering Law, Crime, Criminology & Criminal Justice

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ANALISIS E-GOVERNMENT DALAM PENINGKATAN PELAYANAN PUBLIK PADA DINAS KOMUNIKASI DAN INFORMATIKA PROVINSI SULAWESI TENGAH Risnandar, Risnandar
Katalogis Vol 2, No 7 (2014)
Publisher : Katalogis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (232.186 KB)

Abstract

Theobjective of this research was to find out the implementation of e-Goverment in increasingpublic service at comunication and information official of central Sulawesi province. The Main theory employed was e-goverment theory covers (1) content development , (2) competency building, (3) Connectivity, (4) Cyber Laws, (5) citizen Interfaces, and (6) Capital. It was also supported by President Instruction No. 3 year 2003. The research method employed was qualitative research setting. The research setting  was at communication and information official of central Sulawesi province with 5 informants who chosen purposively as the sample. The tehnique of data collection were interview, observation and documentation. The technique of data analysis was descriptive model by Robson With case study approach. The research results showed that the implementation of e-government in Increasing Public Service at Comunication and Information Official of Central Sulawesi  Province  have  run  well  enough  such  as  Capital  and  Content  Development  and Connectivity even tough still need improvement. Competency Building and Citizen interfaces were less cinsidered where as Cyber Laws was not judged because it was nationally so it needed to be improved by Central Government. Primarily, there was two issues encountered in conducting the main  duty  and  function  of  e-Government  section.  The  first  was  infrastructure  availability, supporting facility such as server, computer, and website from Communication and Information Official were lack. The second was human resources availability particulary those had IT background and technical officers to solve administration and digital information issues. To solve the  issues,  e-Government  had  already  done  some  efforts  at  Communication  and  Information Official of Central of Sulawesi Province. The first efforts was providing and requesting infrastructure availability,  supporting  facility of e-Government.  The second  was  asking  to  the secretariat which would be forwarded by Regional Officery Board to provide technical and IT officers to proceed electronic information and administration.
Kombinasi K-NN dan Gradient Boosted Trees untuk Klasifikasi Penerima Program Bantuan Sosial Elly Firasari; Umi Khultsum; Monikka Nur Winnarto; Risnandar Risnandar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0813087

Abstract

Kemiskinan bagi pemerintah Indonesia termasuk masalah yang sulit untuk diselesaikan. Upaya yang dilakukan pemerintah dalam mengatasi kemiskinan di Indonesia yaitudengan  program bantuan sosial meliputiBLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), dan lain lain. Dalam Pelaksanaan program bantuan sosial saat masih sangat terbatas sehingga dalam penerimaan program bantuan tidak tepat sasaran. Data mining membantu untuk menentukan keputusan dalam memprediksi data di masa yang akan datang. Gradient Boosted Trees dan K-NN merupakan salah satu metode data mining untuk klasifikasi data. Masing-masing metode tersebut memiliki kelemahan. Gradient Boosted Trees menghasilkan nilai persentase akurasi lebih rendah dibanding metode K-NN. Dari permasalahan tersebut maka diusulkan metode kombinasi K-NN dan Gradient Boosted Trees untuk meningkatkan akurasi pada pelaksanaan program bantuan sosial agar tepat sasaran. Metode K-NN, Gradient Boosted Trees, K-NN-Gradient Boosted Treesdilakukan pengujian pada data yang sama untuk mendapatkan hasil perbandingan nilai akurasi. Hasil pengujian membuktikan bahwa kombinasi tersebut menghasilkan nilai persentase yang tinggi dibanding metode K-NN atau Gradient Boosted Trees yaitu 98.17%.AbstractPoverty for the Indonesian government is a problem that is difficult to solve. The efforts made by the government in overcoming poverty in Indonesia are through social assistance programs including BLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), and others. In the implementation of the social assistance program when it was still very limited, the acceptance of the aid program was not on target. Data mining helps to determine decisions in predicting data in the future. Gradient Boosted Trees and K-NN are data mining methods for data classification. Each of these methods has weaknesses. Gradient Boosted Trees produce lower accuracy percentage values than the K-NN method. From these problems, a proposed method of combination of K-NN and Gradient Boosted Trees is used to improve the accuracy of the implementation of social assistance programs so that it is right on target. The K-NN, Gradient Boosted Trees, and K-NN-Gradient Boosted Trees methods are tested on the same data to get a comparison of the accuracy values. The test results prove that the combination produced a high percentage value compared to the K-NN or Gradient Boosted Trees method that is 98.17%.
Analisis Kinerja Algoritma CART dan Naive Bayes Berbasis Particle Swarm Optimization (PSO) untuk Klasifikasi Kelayakan Kredit Koperasi Eko Arif Riyanto; Tri Juninisvianty; Doddy Ferdian Nasution; Risnandar Risnandar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0812988

Abstract

Koperasi memiliki peranan penting terutama untuk masyarakat kecil dan menengah. Salah satu kendala yang dirasakan oleh koperasi adalah analisa pemberian kredit yang dilakukan secara manual dan hanya berdasarkan kedekatan secara personal dengan anggota sehingga menyebabkan terjadinya kredit – kredit  macet yang tidak diduga. Oleh karena itu perlu adanya perhitungan yang sistematis dalam pemberian kredit kepada para peminjam. Teknik klasifikasi data mining merupakan salah satu teknik yang bisa digunakan dalam menentukan kelayakan kredit. Tujuan dari penelitian ini adalah untuk menentukan metode terbaik untuk klasifikasi kelayakan kredit koperasi menggunakan software Rapidminer dengan membandingkan perhitungan algoritma CART, Naive Bayes, optimasi CART + PSO, dan Naive Bayes + PSO. Data yg digunakan adalah 113 data anggota koperasi. Dari perhitungan dengan acuan kriteria pekerjaan, pendapatan, usia, jenis kelamin, jumlah pinjaman, jangka waktu, akan memperoleh metode terbaik untuk klasifikasi kelayakan kredit. Metode terbaik yang dihasilkan dari penelitian ini adalah metode Naive Bayes + PSO. Nilai accuracy yang diperoleh dari penelitian ini adalah 96,43%, nilai recall 94,12%, niilai precision 100%. Dengan nilai AUC sebesar 0,963 , penelitian ini termasuk dalam klasifikasi baik. Hasil dari penelitian ini dapat digunakan sebagai salah satu pertimbangan untuk klasifikasi kelayakan kredit pada koperasi simpan pinjam. AbstractCredit Union have an important role especially to the small and medium society. One of the problem  that credit union have is an analyzing credit manually and only based on closeness personally that can be an unexpected bad credit for credit union. Therefore, it is necessary to build a systematic calculation to give a credit for debtor. Classification technic in data mining is one of the technic that can use to classify the credit properness. The purpose of this study is to get the best method to classify the credit properness using Rapidminer by compare the calculation of CART, Naive Bayes and the optimization of CART+PSO and Naive Bayes+PSO. The study using 113 data member of credit union. From the calculation reference to the criteria of occupation, income, age, gender, loan amount, loan term, will get the best method for this study. The best method from this study is the Naive Bayes+PSO. The accuracy value obtained from this study was 96.43%, the recall value was 94.12%, and the precision value is 100%. AUC value of 0.963 indicates that this study is included in the good classification. The results of this study can be used as a consideration for the classification of the credit properness of credit union.
Analisis Loyalitas Pelanggan Berbasis Model Recency, Frequency, dan Monetary (RFM) dan Decision Tree pada PT. Solo Basri Basri; Windu Gata; Risnandar Risnandar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 5: Oktober 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020752284

Abstract

Perkembangan bisnis alat tulis kantor dan sekolah saat ini banyak yang menjanjikan, maka banyak bermunculan pemasok baru dalam bisnis Alat Tulis Kantor dan Sekolah (ATKS). PT Solo yang bergerak di bidang bisnis ATKS harus memiliki strategi dalam setiap persaingan usaha, khususnya dalam meraih loyalitas pelanggan. Loyalitas pelanggan sering dipengaruhi oleh faktor jumlah aktivitas transaksi, nilai nominal transaksi, waktu transaksi di perusahaan, dan atribut outlet. Penelitian ini mengusulkan model Recency, Frequency, dan Monetary (RFM) yang dikombinasikan dengan Decision Tree. Model RFM digunakan untuk proses klasterisasi data pelanggan berdasarkan jumlah transaksi, nilai nominal transaksi, waktu transaksi, dan atribut outlet. Sedangkan Decision Tree dapat menggambarkan tingkat loyalitas pelanggan. Data transaksi dalam penelitian ini dilakukan sepanjang 1 Januari hingga 31 Desember 2018 terhadap 1.203 pelanggan dan 18.087 transaki melalui faktur pembelian. Hasil penelitian ini menunjukan bahwa state-of-the-art pada model RFM dan Decision Tree yang diusulkan lebih unggul dibandingkan hanya dengan menggunakan model RFM saja. Cluster ke-1 memiliki 860 pelanggan menghasilkan loyalitas pelanggan sedang (biru), cluster ke-2 memiliki 69 pelanggan menghasilkan loyalitas pelanggan yang tinggi (hijau), dan cluster ke-3 memiliki 274 pelanggan menghasilkan loyalitas pelanggan yang rendah (merah). Model klasterisasi RFM dan klasifikasi Decision Tree telah menghasilkan atribut outlet yang berpengaruh terhadap nilai akurasi sebesar 67,54%. Abstract The development of office and school stationery business at this time, many promising, so many new suppliers have sprung up in the office and school stationery business. PT Solo, which has the office and school stationery business, must have a strategy in every business competition, especially in achieving customer loyalty. Customer loyalty is often influenced by factors in the number of transaction activities, transaction nominal value, transaction time at the company, and outlet attributes. This research proposes a Recency, Frequency, and Monetary (RFM) model combined with a Decision Tree. RFM model is used to process customer data clustering based on number of transactions, transaction nominal value, transaction time, and outlet attributes. Whereas Decision Tree can describe the level of customer loyalty. Transaction data in this study were conducted from 1 January to 31 December 2018 to the 1,203 customers and 18,087 transactions through purchase invoices. The results of this study indicate that the state-of-the-art in the proposed RFM and Decision Tree models is outperform compared to only using the RFM model. Cluster 1 has 860 customers resulting in moderate customer loyalty (blue), Cluster 2 has 69 customers resulting in high customer loyalty (green), and Cluster 3 has 274 customers resulting in lower customer loyalty (red). RFM clustering model and Decision Tree classification have produced outlet attributes that affect the accuracy value of 67.54%.
Co-Authors Basri Basri Doddy Ferdian Nasution Eko Arif Riyanto Firasari, Elly Monikka Nur Winnarto Tri Juninisvianty Umi Khultsum Windu Gata