Article Per Year (5 Year)
p-Index From 2019 - 2024
1.789
P-Index
This Author published in this journals
All Journal IAES International Journal of Robotics and Automation (IJRA) Jurnal Informatika POSITIF Knowledge Engineering and Data Science Jusikom : Jurnal Sistem Komputer Musirawas Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) J-Icon : Jurnal Komputer dan Informatika
Suprapty, Bedi
Politeknik Negeri Samarinda
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Pengelompokan Bidang Keilmuan Di Teknologi Informasi Dengan Metode K-Means Dan Optimasi Simple Additive Weighting (Saw) Dalam Penentuan Kesesuain Terhadapa Keilmuan Hariyanto, Dedi; Malani, Rheo; Suprapty, Bedi
Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Vol 3, No 1 (2018): Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi (SAKTI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (274.504 KB)

Abstract

POLNES JURTI terdiri dari tiga prodi, terdapat 9 Bidang kompetensi keilmuan yaitu, Mobile Computing, Computer Controlled Infrastructure, Computer Vision, Robotic & Artificial Intelligent, Advanced Applied Computer, Human Computer Interaction, Intelligent Computing, Cloud Computing, Multimedia. Pada masing-masing prodi memiliki area kompetensi. Tujuan penelitian mengarahkan ke bidang kompetensi keilmuan yang lebih sesuai, algortima K-means merupakan sebuah algoritma yang mengkelompokan data berdasarkan jarak terdekat dari suatu cluster, MAPE digunakan sebagai perhitungan error pada masing-masing cluster dari perhitungan, SAW adalah metode penjumlahan terbobot, SAW pada penelitian ini dilakukan untuk menentukan responden yang paling sesuai dengan hasil cluster pada K-means. hasil perhitungan K-means tidak bisa menentukan cluster sesuai dengan masing-masing prodi. Karna K-means sendiri menghitung berdasarkan hasil dari nilai Res, dan membandingkan nilai tersebut pada masing-masing cluster, hasil perhitungan error dengan menggunakan MAPE, perhitungan error menunjukkan bahwa cluster pada K-means sangat akurat amat akurat dalam pembagian cluster berdasarakan hasil perhitungan dari kuisioner, Hasil dari perhitungan SAW menujukan bahwa ada nilai yang sama pada salah satu responden mengakibatkan rangking pada suatu cluster menjadi sama, seperti pada cluster CCI. Pada rangking 3 dan 7 masingmasing mempunya dua responden, Hal tersebut membuat perhitungan SAW pada cluster menjadi kurang optimal, karna dalam satu rangking terdapat dua responden.
Perbandingan Metode K-Means dan Fuzzy C-Means Untuk Pengelompokan Pegawai Berdasarkan Nilai Kinerja dan Tingkat Kedisiplinan Pegawai Wikarno, Wikarno; Malani, Rheo; Suprapty, Bedi
Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Vol 3, No 1 (2018): Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi (SAKTI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (461.3 KB)

Abstract

Bank Indonesia (BI) adalah bank sentral Republik Indonesia. Sebagai bank sentral, BI membutuhkan pegawai– pegawai dengan kompetensi terbaik dalam bidangnya masing– masing serta perilaku yang baik. Untuk mengetahui kualifikasi setiap pegawai dibutuhkan sebuah pengelompokan/clustering. Melalui proses penilaian kinerja serta tingkat kedisiplinan pegawai dilakukan proses pengelompokan agar dapat diketahui cluster–cluster yang terdapat didalamnya. Pembagian cluster pada penelitian ini mencirikan kualifikasi pegawai, dimana cluster 1 berisi pegawai dengan kualifikasi terbaik, cluster 2 berisi pegawai dengan kualifikasi baik, cluster 3 berisi pegawai dengan kualifikasi cukup, cluster 4 berisi pegawai dengan kualifikasi kurang dan cluster 5 berisi pegawai dengan kualifikasi Buruk. Rata-rata Persentase MAPE untuk keseluruhan cluster pada metode K-means lebih kecil dibandingkan dengan metode Fuzzy C-means. Persentase MAPE pada metode K-means sebesar 18,21%  sementara pada metode Fuzzy C-means sebesar 21,07%. Rata-rata varian jarak antar anggota pada masing-masing cluster untuk keseluruhan cluster pada metode K-means lebih kecil dibandingkan dengan metode Fuzzy C-means. Rata-rata varian jarak pada metode K-means sebesar 0,350 sementara pada metode Fuzzy C-means sebesar 0,863. Rata-rata jarak antar cluster pada keseluruhan jarak antar tiap-tiap cluster pada metode K-means lebih besar dibandingkan dengan metode Fuzzy C-means. Rata-rata jarak antar cluster pada metode K-means sebesar 9,227 sementara pada metode Fuzzy C-means sebesar 6,465.
SELEKSI CALON PENERIMA BEASISWA PT. ADIMITRA BARATAMA NUSANTARA MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING Suprapty, Bedi; Utami, Lia Dwi; Putra, Arief Bramanto Wicaksono
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 5 No 2 (2019): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v5i2.791

Abstract

PT. Adimitra Baratama Nusantara is a coal company in Sangasanga that provides scholarship funds to students in Sangasanga. The large number of scholarship registrants makes it difficult for companies to handle data processing, so software is needed to facilitate the processing of the data. This research was conducted with the aim of constructing a decision model to determine the scholarship recipients using the SAW method so that the selection process for prospective scholarship recipients can be completed precisely, quickly, and more procedurally. Determination of scholarship recipients is determined from several criteria, among others; parents' income, number of family members, home electrical power, average report card grade, class, and extracurricular numbers followed. Then to design an application requires several stages, namely by making Context Diagrams, Data Flow Diagrams, and Entity Relationship Diagrams and applying them to a software / program that will be built using Visual Basic 6 programming language along with Microsoft Access database. Decision model for scholarship recipients at PT. Adimitra Baratama Nusantara with the Simple Additive Weighting (SAW) Method resulted in 10 applicants being selected as scholarship recipients and 20 applicants declared to have failed the selection process
Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment Mulyanto Mulyanto; Bedi Suprapty; Arief Bramanto Wicaksono Putra; Achmad Fanany Onnilita Gaffar
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a17389

Abstract

In addition to the focus of research selected as a Final Project material, the selection of lecturers as student's supervisor becomes very important. The lecturer's competence related to the focus of student research and the supervising style of lecturers is also very influential on the final results. Measurement of style appropriateness between students' learning styles and supervising lecturers' styles can benchmark the quality of the final project's implementation, especially higher education institutions. This study has applied fuzzy-based assessment to build objective perceptions of students' learning characteristics and lecturers' characteristics (Visual (V), Auditory (A), Kinesthetic (K)) as supervisors through questionnaire processing that has designed in such away. Hence, it is suitable for this study. The measuring technique of the percentage of overlapping areas under the curves and the correlation test between a pair of curves have been used as performance measurement metrics. In general, the study results indicate a significant level of coverage adequacy for all research variables regarding existing conditions. It means that the process of Final Project activities in terms of students' and lecturers' learning characteristics as supervisors and their distribution is at a reasonable level (88.38%). It has also been shown by the results of the correlation test of the appropriateness of choice, both supervisors selected by students (0.8657) and students chosen by lecturers (0.9897) who are at a very significant level of similarity. Correlation tests conducted for similarities between students' and lecturers' learning characteristics as supervisors show almost no significant correlation between them (0.4064).
SELEKSI CALON PENERIMA BEASISWA PT. ADIMITRA BARATAMA NUSANTARA MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING Bedi Suprapty; Lia Dwi Utami; Arief Bramanto Wicaksono Putra
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 5 No 2 (2019): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v5i2.791

Abstract

PT. Adimitra Baratama Nusantara is a coal company in Sangasanga that provides scholarship funds to students in Sangasanga. The large number of scholarship registrants makes it difficult for companies to handle data processing, so software is needed to facilitate the processing of the data. This research was conducted with the aim of constructing a decision model to determine the scholarship recipients using the SAW method so that the selection process for prospective scholarship recipients can be completed precisely, quickly, and more procedurally. Determination of scholarship recipients is determined from several criteria, among others; parents' income, number of family members, home electrical power, average report card grade, class, and extracurricular numbers followed. Then to design an application requires several stages, namely by making Context Diagrams, Data Flow Diagrams, and Entity Relationship Diagrams and applying them to a software / program that will be built using Visual Basic 6 programming language along with Microsoft Access database. Decision model for scholarship recipients at PT. Adimitra Baratama Nusantara with the Simple Additive Weighting (SAW) Method resulted in 10 applicants being selected as scholarship recipients and 20 applicants declared to have failed the selection process
KLASIFIKASI DOKUMEN LAYANAN SISTEM DAN DATA PRODUK MENGGUNAKAN FUZZY C-MEANS CLUSTERING Bedi Suprapty; Rheo Malani
Jusikom : Jurnal Sistem Komputer Musirawas Vol 6 No 2 (2021): Jusikom : Jurnal Sistem Komputer Musirawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v6i2.1329

Abstract

System Application and product (SAP) merupakan suatu produk perangkat lunak yang berguna untuk melakukan kontrol terhadap dokumen pelaporan kerusakan ataupun gangguan pada layanan yang di ajukan pada tiap unit departemen. Dalam penelitian ini di butuhkan solusi untuk melakukan pengelompokkan/clustering. Attribut yang digunakan pada penelitian ini berupa layanan aplikasi, dimana atribut 1 berisi SLA, atribut 2 berisi Maot. Cluster di bagi menjadi 4 level antara lain pada level 1 (Helpdesk TI), level 2 (Kasie layanan TI), level 3 (Kabag TI), dan level 4 (Manajer TI). Fuzzy C-Means (FCM) adalah metode yang digunakan dalam menyelesaikan solusi berupa hasil pengelompokkan layanan aplikasi. Penentuam centroid awal di lakukan dengan mengambil nilai rentang antara nilai Min dan Max pada data setiap attribut kemudian dibandingkan dengan jumlah cluster sehingga menghasilkan interval untuk setiap cluster. Adapun hasil dari penelitian ini pada cluster level 1 dan 2 tidak ada komplain dari pengguna masalah kerusakan ataupun gangguan pada layanan aplikasi tersebut. Di cluster level 3 terdapat 2 layanan aplikasi mengalami kerusakan ataupun gangguan. Jika layanan aplikasi tersebut tidak dapat diselesaikan pada batas waktu SLA yg ditentukan, maka pengguna tersebut berhak komplain ke Kabag TI. Begitu juga pada cluster level 4 sebanyak 22 layanan aplikasi mengalami kerusakan ataupun gangguan jika aplikasi tersebut belum dapat diselesaikan pada batas waktu SLA yg ditentukan maka pengguna tersebut juga berhak komplain ke Manajer TI. Rata – rata persentase MAPE pada keseluruhan clustering adalah 13,20%.
Perbandingan Metode SAW dan TOPSIS untuk penentuan Dosen Terbaik pada Jurusan Teknologi Informasi Politeknik Negeri Samarinda Aldy Gustiannur Rachmat; Bedi Suprapty; Abdul Najib
Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Vol 3, No 1 (2018): Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi (SAKTI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.926 KB)

Abstract

Dosen merupakan pendidik dan ilmuwan yang bertugas menyebar luaskan ilmunya melalui pendidikan penelitian dan pengabdian, hal ini tertuang dalam Undangundang Nomor 14 Tahun 2005 tentang Guru dan Dosen.. Menurut Undang-undang Republik Indonesia No 14 tahun 2005 tentang Guru dan Dosen pasal 51 Ayat (1) Butir b, bahwa dalam melaksanakan tugas keprofesionalan dosen berhak mendapatkan promosi dan penghargaan sesuai dengan tugas prestasi kerja. Untuk menentukan keputusan terhadap prestasi dosen atau menentukan dosen terbaik dibutuhkan Sistem Pendukung Keputusan (SPK), dalam menentukan dosen terbaik dibutuhkan alternatif yang sesuai, untuk itu alternatif ditentukan berdasarkan referensi yang berasal dari jurnal maupun pihak manajemen kampus. Adapun alternatif yang digunakan dalam menentukan dosen terbaik adalah Kewibawaan, Disiplin, Penguasaan Materi, Pemanfaatan media dan teknologi pembelajaran, Penelitian Nasional, Penelitian Internasional dan Pengabdian.  Dalam menentukan keputusan pada suatu masalah berdasarkan analisa pribadi tanpa suatu metode biasanya tingkat kesalahan yang diperoleh tinggi sehingga hal ini membahayakan dan berujung pada penyesalan.  Ada banyak metode yang dapat digunakan dalam SPK namun yang sering digunakan yakni Simple Additive Weighting (SAW) dan Topsis. Karena itu untuk mengetahui metode yang baik dari 2 metode tersebut, maka untuk membandingkan kedua metode dilakukan perhitungan rentang nilai antar kedua metode.
KLASIFIKASI MINAT SISWA UNTUK PROGRAM STUDI JURUSAN TEKNOLOGI INFORMASI - POLITEKNIK NEGERI SAMARINDA MENGGUNAKAN METODE FUZZY C-MEANS CLUSTERING Bedi Suprapty; Fariyanti Fariyanti
J-Icon : Jurnal Komputer dan Informatika Vol 8 No 1 (2020): Maret 2020
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v8i1.2184

Abstract

Students who have graduated from high school or high school will continue to a higher level such as Samarinda State Polytechnic. Samarinda State Polytechnic consists of several Departments approved by the Information Technology department. The Information Technology Department has 4 Study Programs including; D3 Informatics Engineering, D3 Computer Engineering, D4 Multimedia Information Technology and D4 Computer Technology Engineering. This research was conducted by classifying specialization of students who would continue their studies to the Department of Information Technology, Samarinda State Polytechnic. Sources of data obtained from the questionnaire. Data collection was carried out by questionnaire method, the questionnaire consisted of 15 questions and had 5 criteria. Each criterion has 3 questions. The questionnaire was distributed to 160 high school and vocational high school students in the city of Samarinda, East Kalimantan. Clusters in this study are divided into 4, namely cluster 1, an interest in the D3 Study Program in Informatics Engineering, cluster 2 an interest in the D3 Study Program in Computer Engineering, cluster 3 an interest in the D4 Study Program in Multimedia Information Technology and cluster 4 an interest in D4 in Computer Engineering Technology. Fuzzy C-means method is used in resolving these complications where the results of clustering cluster 1 consists of 41 students, cluster 2 consists of 46 students, cluster 3 consists of 21 students, cluster 4 consists of 52 students. The average MAPE percentage for the whole cluster is 27.07%.
PENGELOMPOKAN SEBARAN TRANSFORMATOR DISTRIBUSI BERDASARKAN KAPASITAS DAYA MENGGUNAKAN METODE NAÏVE BAYES Studi Kasus: PT. PLN RAYON KOTA SAMARINDA Rheo Malani; Bedi Suprapty
J-Icon : Jurnal Komputer dan Informatika Vol 8 No 1 (2020): Maret 2020
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v8i1.2185

Abstract

ABSTRACT Human needs for energy are mostly obtained from electrical energy, both for daily needs and for industrial needs. PT. PLN (Persero) is one of the state electricity companies that serves the community's need for electricity. Transformer or better known as "transformer" or "transformer" is actually an electrical device that converts AC power at one voltage level to one voltage level based on the principle of electromagnetic induction without changing its frequency. Because of the lack of distribution of transformers around the Samarinda area, it can result in electricity demand services to the community. Therefore we need a method that can facilitate the distribution of PT. PLN Rayon Kota Samarinda, one of the methods is by applying Naïve Bayes. The purpose of this study is to facilitate the distribution in each region and the type of transformer used. The results of calculations using the Naïve Bayes method, obtained the probability of grouping the training data is P (160) = 0.006441224, P (100) = 0.016304348, P (80) = 0.001610306, P (50) = 0.001610306, P (40) = 0.000402576, P P (20) = 0,000679348. From the calculation results, it appears that the probability value P (100) is more dominant, then 100 is recommended for real consumption which is used as training data. The Naïve Bayes method produces an accuracy rate of 92%.
Inter-Frame Video Compression based on Adaptive Fuzzy Inference System Compression of Multiple Frame Characteristics Arief Bramanto Wicaksono Putra; Rheo Malani; Bedi Suprapty; Achmad Fanany Onnilita Gaffar; Roman Voliansky
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p1-14

Abstract

Video compression is used for storage or bandwidth efficiency in clip video information. Video compression involves encoders and decoders. Video compression uses intra-frame, inter-frame, and block-based methods.  Video compression compresses nearby frame pairs into one compressed frame using inter-frame compression. This study defines odd and even neighboring frame pairings. Motion estimation, compensation, and frame difference underpin video compression methods. In this study, adaptive FIS (Fuzzy Inference System) compresses and decompresses each odd-even frame pair. First, adaptive FIS trained on all feature pairings of each odd-even frame pair. Video compression-decompression uses the taught adaptive FIS as a codec. The features utilized are "mean", "std (standard deviation)", "mad (mean absolute deviation)", and "mean (std)". This study uses all video frames' average DCT (Discrete Cosine Transform) components as a quality parameter. The adaptive FIS training feature and amount of odd-even frame pairings affect compression ratio variation. The proposed approach achieves CR=25.39% and P=80.13%. "Mean" performs best overall (P=87.15%). "Mean (mad)" has the best compression ratio (CR=24.68%) for storage efficiency. The "std" feature compresses the video without decompression since it has the lowest quality change (Q_dct=10.39%).
Co-Authors Abdul Najib Achmad Fanany Onnilita Gaffar Aldy Gustiannur Rachmat Fariyanti Fariyanti Lia Dwi Utami Malani, Rheo Muhammad Taufiq Sumadi Mulyanto Mulyanto Roman Voliansky Syamsul Arifin Utami, Lia Dwi Wikarno, Wikarno