Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

1.713

P-Index

This Author published in this journals

All Journal Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Fisika FLUX KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) RAGAM: Journal of Statistics and Its Application

Oni Soesanto

Unknown Affiliation

Author-ID : 384194

Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 30 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3

PENERAPAN METODE LEARNING VECTOR QUANTIZATION (LVQ) PADA PREDIKSI JURUSAN DI SMA PGRI 1 BANJARBARU Risky Meliawati; Oni Soesanto; Dwi Kartini
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 3, No 1 (2016)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v3i1.31

Determination of majors in SMA PGRI 1 Banjarbaru for students grade XI is still using a manual process that currently have problems with majors process that takes a long time. In this study aims to determine applicability methods Learning vector quantization (LVQ) the prediction determination majors in SMA PGRI 1 Banjarbaru of accuracy values obtained. Artificial Neural Networks supervised (supervised) as LVQ (Learning Vector Quantization) is a method of classification patterns of each unit of output represents a category or a particular group. From the results predicted during the first year of data known that the accuracy approaching the actual results with different number of iterations is the accuracy of 79.31% for iteration = 60 and 90. In the study with the alpha value changes obtained with accuracy approaching actual results are Accuracy 75.86% with a value of alpha (Î±) = 0.14. Conclusion in This study has successfully predict the determination of majors in SMA PGRI 1 Banjarbaru by using the Learning vector quantization (LVQ). Keywords: Artificial Neural Networks, Learning Vector Quantization (LVQ), Majors Penentuan penjurusan di SMA PGRI 1 Banjarbaru untuk siswa naik kelas XI masih menggunakan proses manual yang saat ini memiliki kendala dengan proses penjurusan yang membutuhkan waktu lama. Pada penelitian ini bertujuan Untuk mengetahui dapat diterapkannya metode Learning vector Quantization (LVQ) pada prediksi penetuan jurusan di SMA PGRI 1 Banjarbaru dari nilai akurasi yang didapat. Jaringan Syaraf Tiruan terawasi (supervised) seperti LVQ (Learning Vector Quantization) adalah suatu metode klasifikasi pola yang masing-masing unit output mewakili kategori atau kelompok tertentu. Dari hasil prediksi selama data 1 tahun diketahui bahwa nilai akurasi yang mendekati dengan hasil sebenarnya dengan jumlah iterasi yang berbeda adalah akurasi 79,31% untuk iterasi= 60 dan 90. Pada penelitian dengan nilai alpha yang berubah didapat akurasi yang mendekati dengan hasil sebenarnya adalah Akurasi 75,86% dengan nilai alpha (Î±) = 0,14. Kesimpulan penelitian ini telah berhasil melakukan prediksi pada penentuan jurusan di SMA PGRI 1 Banjarbaru menggunakan metode Learning vector quantization (LVQ). Kata kunci : Jaringan Syaraf Tiruan, Learning Vector Quantization (LVQ), Penjurusan

ALGORITMA K-MEANS CLUSTERING DALAM PENGOLAHAN CITRA DIGITAL LANDSAT Nur Ridha Apriyanti; Radityo Adi Nugroho; Oni Soesanto
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 2, No 2 (2015)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v2i2.22

Digital image processing can now be done with a variety of assistive software, one of which ArcGIS. At ArcGIS there are some features of image classification with multiple algorithms, but there is an algorithm that has not been used, this is K-Means algorithm. From the test results are obtained 12 land cover classes as follows pastures, airports, mining, open land, plantations, swamps, bushes, shrubs, settlements, plantations, dryland agriculture, and vegetated land. Results of field inspections showed 94.4% fit, and 5.6% did not correspond to actual field conditions. Keywords : Digital Image Processing, K-Means Algorithm, Clustering. Pengolahan citra digital saat ini bisa dilakukan dengan berbagai macam software bantu, salah satunya ArcGIS. Pada ArcGIS terdapat beberapa fitur klasifikasi citra dengan beberapa algoritma, namun ada satu algoritma yang belum digunakan yaitu algoritma K-Means. Dari hasil pengujian didapatkan 12 kelas penutupan lahan sebagai berikut padang rumput, bandara udara, pertambangan, lahan terbuka, hutan tanaman, rawa, semak, belukar, pemukiman, perkebunan, pertanian lahan kering, dan lahan bervegetasi. Hasil pengecekan lapangan menunjukkan 94,4% sesuai, dan 5,6% tidak sesuai dengan kondisi lapangan yang sebenarnya. Kata kunci : Pengolahan Citra Digital, Algoritma K-Means, Clustering

ENTROPY-BASED FUZZY AHP SEBAGAI PENDUKUNG KEPUTUSAN PENEMPATAN BIDAN DI KOTA BANJARBARU Siti Hatimah Rahmadaniah; Oni Soesanto; Dwi Kartini
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 2, No 2 (2015)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v2i2.27

Banjarbaru City Health Office is a government agency that plays an important role in health development in Banjarbaru. Support system midwife placement in the Health Service Banjarbaru done by following the specified criteria. However, the system has not been run in accordance with these criteria, but every midwife who will be placed in primary and villages are given the freedom to choose from one health center placement options, and direct placement was decided on the option selected health centers midwives. Because of the ineffectiveness of the existing system at the Department of Health Banjarbaru it will be designed and built "Midwives Placement Decision Support Systems in Banjarbaru with Method Using Entropy-Based Fuzzy AHP (Analytical Hierarchy Process)" to determine the placement of midwives in accordance with the criteria that have been determined. From the research and observations of the system that has been created, it can be seen that the Decision Support System Placement Midwives using Entropy-Based Fuzzy AHP is a decision that is in accordance with the number of midwives needed by each health center. Keywords : Decision Support System , Placement Midwives , Entropy Method. Dinas Kesehatan Kota Banjarbaru merupakan suatu instansi pemerintah yang berperan penting dalam pembangunan kesehatan di Kota Banjarbaru . Sistem pendukung penempatan bidan di Dinas Kesehatan Banjarbaru dilakukan dengan mengikuti kreteria yang sudah ditentukan. Akan tetapi sistem tersebut belum berjalan sesuai dengan kriteria tersebut melainkan setiap bidan yang akan ditempatkan dipuskesmas dan desa diberikan kebebasan untuk memilih dari salah satu puskesmas pilihan penempatan, dan langsung diputuskan penempatan pada puskesmas pilihan yang dipilih bidan. Oleh karena tidak berjalannya sistem yang sudah ada pada Dinas Kesehatan Banjarbaru maka akan dirancang dan dibangun â€œSistem Pendukung Keputusan Penempatan Bidan di Kota Banjarbaru dengan Menggunakan Metode Entropy-Based Fuzzy AHP (Analytical Hierarchy Process)â€ untuk menentukan penempatan bidan yang sesuai dengan kreteria yang sudah ditentukan. Dari hasil penelitian dan pengamatan dari sistem yang telah dibuat, dapat diketahui bahwa Sistem Pendukung Keputusan Penempatan Bidan menggunakan metode Entropy-Based Fuzzy AHP ini mendapatkan keputusan yang sesuai dengan jumlah bidan yang diperlukan oleh tiap-tiap puskesmas. Kata kunci : Sistem Pendukung Keputusan, Penempatan Bidan, Metode Entropy.

K-MEANS UNTUK KLASIFIKASI PENYAKIT KARIES GIGI Novita Meisida; Oni Soesanto; Heru Kartika Candra
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 1, No 1 (2014)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v1i1.2

Oral health problems , a concern which is very important in health development. Results of Household Health Survey report in 2001 showed that oral health in Indonesia are things that need attention. Based on the report of Poly Teeth ITS Medical Center 2009 obtained data characteristics of dental caries and dental caries classes based on the anatomy of the JV Black. From these data can be classification using K - Means clustering method. K -Means clustering method is used for grouping data partitioning system, where data in one group have similar characteristics to each other, and have different characteristics with other groups. Classification results using K - Means Clustering method will be compared with the results of reports Poly Teeth ITS Medical Center 2009, to compare and get result from accuracy of the K - Means Clustering. Keywords: K-Means, Caries, Classification Masalah kesehatan gigi dan mulut, menjadi perhatian yang sangat penting dalam pembangunan kesehatan. Hasil laporan survei Kesehatan Rumah Tangga tahun 2001 menunjukkan bahwa kesehatan gigi dan mulut di Indonesia merupakan hal yang perlu diperhatikan. Berdasarkan hasil laporan Poli Gigi Medical Center ITS 2009 didapatkan data-data berupa data karakteristik karies gigi dan kelas-kelas karies gigi berdasarkan anatomi J. V. Black. Dari data-data tersebut dapat dilakukan pengklasifikasian dengan menggunakan metode Clustering K-Means. Metode Clustering K-Means digunakan karena K-Means melakukan pengelompokkan data dengan sistem partisi, dimana data dalam satu kelompok memiliki karakteristik yang sama satu sama lainnya, dan memiliki karekteristik berbeda dengan kelompok lainnya. Hasil pengklasifikasian metode ClusteringK-Means dibandingkan hasilnya dengan laporan Poli Gigi Medical Center ITS 2009, untuk membandingkan keakuratanClustering K-Means. Kata Kunci: K-Means, Karies Gigi, Klasifikasi

RANCANG BANGUN APLIKASI PENGENALAN POLA SIDIK JARI Ryan Wahyudi; Oni Soesanto; Muliadi Muliadi
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 2, No 1 (2015)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v2i1.19

Biometrics is a method of recognition of an identity based on human physical characteristics such as the face, fingerprint, hand geometry, retina, and voice. Biometric identification that commonly used is the fingerprint recognition. Fingerprint identification process can be accelerated by reducing the number of fingerprint comparisons, splitting fingerprint databases into a number of classes based on pre-defined classes, such as fingerprint patterns. Fingerprint patterns are divided into five categories: Whorls, Right Loops, Left Loops, Arch, and Tented Arch. One of the pattern recognition techniques (fingerprint) is using neural network. This research developed a RBF (Radial Basis Function) neural network, which is known as SLFNs (Single Hidden Layer Feed-forward Neural Networks) that reliable in pattern recognition. The use of ELM (Extreme Learning Machine) algorithm on RBF network is an alternative to avoid long computation in the absence of adjustment weights during the training process so that the computing time relatively short. OLS (Orthogonal Least Square) is used to optimize the weights and RBF network simplification. The preprocessing of fingerprint images are grayscalling, histogram equalization, and image sequences block operation. Feature extraction method that used based on the orientation of the dominant direction of the image. One fingerprint image is represented by a value of 256 dominant angle in radians unit. From the results indicate that the ELM-RBF and OLS system can recognize fingerprint patterns with 100% accuracy on the training process, and 60% accuracy in the testing process. Keywords: Fingerprint Pattern Recognition, Extreme Learning Machine, Radial Basis Function, Orthogonal Least Square Biometrik merupakan metode pengenalan identitas seseorang berdasarkan karakteristik fisik manusia misalnya wajah, sidik jari, struktur telapak tangan, letak retina mata, dan suara. Identifikasi biometrik yang umum digunakan saat ini adalah pengenalan sidik jari. Proses identifikasi sidik jari dapat dipercepat dengan cara mereduksi sejumlah sidik jari pembanding, seperti membagi database sidik jari ke dalam sejumlah kelas berdasarkan kelas yang telah didefinisikan sebelumnya, misalnya pola sidik jari. Pola sidik jari dibagi ke dalam lima kategori, yaitu: Whorls, Right Loops, Left Loops, Arch, dan Tented Arch. Salah satu teknik pengenalan pola (sidik jari) adalah dengan jaringan saraf tiruan. Penelitian ini mengembangkan jaringan saraf tiruan RBF (Radial Basis Function), yang dikenal sebagai SLFNs (Single Hidden Layer Feed-forward Neural Networks) yang handal dalam pengenalan pola. Penggunaan algoritma ELM (Extreme Learning Machine) pada jaringan RBF merupakan salah satu alternatif untuk menghindari adanya komputasi yang lama karena tidak adanya penyesuaian bobot selama proses training sehingga waktu komputasi berlangsung relatif lebih singkat. OLS (Orthogonal Least Square) digunakan untuk optimalisasi bobot dan penyederhanaan jaringan RBF. Sebagai proses pengolahan awal citra sidik jari dilakukan proses normalisasi grayscalling, perataan histogram, dan operasi blok. Metode ekstraksi fitur ciri yang digunakan berbasis orientasi arah dominan citra. Satu citra sidik jari diwakili oleh 256 nilai sudut dominan dalam satuan radian. Dari hasil uji coba program menunjukkan bahwa ELM-RBF dan OLS dapat mengenali pola sidik jari dengan akurasi 100% pada proses training dan 60% pada proses testing. Kata kunci: Pengenalan Pola Sidik Jari, Extreme Learning Machine, Radial Basis Function, Orthogonal Least Square

PCA-RBPNN UNTUK KLASIFIKASI DATA MULTIVARIAT DENGAN ORTHOGONAL LEAST SQUARE (OLS) Oni Soesanto
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 4, No 2 (2010): JURNAL EPSILON VOLUME 4 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

This study will examine the PCA-RBPNN (Principal Component Analysis-Radial Basis) Probabilistic Neural Network) for the classification of multivariate data. The Main Component Analysis (PCA) has widely known in statistics as a method used to reduce the input dimension of the data multivariate by minimizing information loss. In this case, PCA is used to reduce dimensional input on the RBPNN neural network. The clustering process and initialization center is done with Self-Organizing Map (SOM). For the determination of weights during the learning process on the RBPNN network, using the Orthogonal Least Square (OLS) algorithm. Furthermore, PCA-RBPNN method is used for the classification of multivariate data. Accuracy of PCA-RBPNN classification is simulated and compared with the usual RBPNN model.

JARINGAN SYARAF TIRUAN RADIAL BASIS PROBABILISTIC UNTUK IDENTIFIKASI MORFOLOGI BENIH PADI Oni Soesanto; Didin H Musrsyidin
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 7, No 2 (2013): JURNAL EPSILON VOLUME 7 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Artificial neural vision and digital image processing machines are alternative methods that can be done to identify and evaluate the diversity of rice varieties. In contrast to direct observational methods that have high levels of subjectivity and chemical methods (PCRs) that are both destructive and expensive, neural network-based visioning machines offer rapid, practical, inexpensive and accurate identification and evaluation systems, and are non-destructive. This paper discusses artificial neural network based vision technology as an alternative technology for the identification of South Kalimantan swamp varieties based on morphological features, ie area, perimeter, major axis, minor axis, circularity, aspect ratio, roundness and feret for each seed sample rice. In this paper, the identification system of rice seed varieties using Radial Basis Probabilistic (RBP) neural network with optimization of hidden center weight using Orthogonal Least Square (OLS) algorithm. From the learning process, the training performance is 88,329% and the testing performance is 88,2091%, with the success rate in the training process from each variety of Bayar Papuyu, Bayar Putih, Yellow Seed, White Seed, Ketan, Siam Gadis, Siam Unus and Karan Hamlet each of 100%; 92.59%; 88.89%; 92.59%, 92.59%, 81.48, 100%; and 100%. For the testing process the success rate of each variety is 100%; 87.50%; 88.89%; 100%, 88.89%, 88.89, 100%; and 100%.

DIAGNOSA PENYAKIT DEMAM BERDARAH DENGUE DENGAN PENDEKATAN FUZZY Mariyati Mariyati; Muhammad Ahsar Karim; Oni Soesanto
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 7, No 2 (2013): JURNAL EPSILON VOLUME 7 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Dengue Hemorrhagic Fever (DHF) is still one of the major public health problems in Indonesia. This study aims to diagnose dengue fever with fuzzy approach. Fuzzy approach used in this research is fuzzy inference system. The process of this fuzzy inference system consists of three main stages of fuzzification, evaluation of rules and inference, and defuzzification. The inference method used is the Tsukamoto Method. The results stated that the basic rules of fuzzy in diagnosing Dengue Hemorrhagic Fever is formed based on information obtained from the results of consultation with two doctors regarding the diagnosis of DHF and WHO 2009. The basic rules of fuzzy formed that is as many as 483 rules. The results showed that the level of fitting diagnosis of febrile illness bleeding dengue based on the results of fuzzy approach with the diagnosis of the doctors by 77%.

ALGORITMA GENETIKA PADA PENYELESAIAN AKAR PERSAMAAN SEBUAH FUNGSI Akhmad Yusuf; Oni Soesanto
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 6, No 2 (2012): JURNAL EPSILON VOLUME 6 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/epsilon.v6i2.87

The Genetic Algorithm is one approach to determining global optimum that is based on the theory of evolution. Outline the steps in this procedure starting with establishing a set of potential solutions and making changes with some iterations with genetic algorithms to get the best solution. Calculation the root of a function is actually a classic problem in mathematics. For that, various methods have been numerically developed. From the results of the implementation of genetic algorithm to find the root of the equation of a function h (x1, x2) = 1000 (x1-2x2) 2+ (1-x1) 2 in can be that FitMax (genome 9) = 10, FitMin (genome 107) = 0, FitAvr = 0.153, FitTot = 30.6, Best Genome: 10011001001000110010, x1 = 1 and x2 = 0.5 and this is the same as the exact value or value actually from the root of the equation

Penyuluhan Asal Usul Prasejarah Bahasa Indonesia Bagi Generasi Z Di Minggu Raya(Bagian 1) Susilo, Tanto Budi; Soesanto, oni; yunus, Rahmat; Akbar, Arief Rahmad Maulana; Hidayat, Yuyun; Rasjava, Achmad Ramadhanna'il; Krisdianto, Krisdianto
Jurnal Pengabdian ILUNG (Inovasi Lahan Basah Unggul) Vol 4, No 1 (2024)
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ilung.v4i1.13296

Abstrak Di muka bumi ini, prasejarah Sapiens merupakan jejak rekam aktifitas manusia dimulai kisaran 250-300 ribu tahun lalu dan sampai saat ini di berbagai kawasan masih berlangsung prasejarah itu, seperti suku-suku terasing di pedalaman hutan Afrika dan hutan Papua. Prsejarah Sapiens dapat dikategorikan berdasarkan prasejarah genetika, prasejarah bahasa dan prasejarah artifak. Khusus bahasa Sapiens di Nusantara (Indonesia) atau prasejarah bahasa Indonesia dimulai sejak diketemukan simbol bahasa (rock art) di Sumatra, di Kalimantan dan di Sulawesi kisaran 60-40 ribu tahun lalu dan diakhiri sejak temuan simbol bahasa (huruf) caraka dan/pallawa kisaran 7-8 M. Program kreatifitas masyarakat (PKM) ini melibatkan generasi Z. Metode structural equation modelling (SEM) digunakan untuk koleksi dan evaluasi data. Hasil uji pretest dan post test ditujukan kepada responden berumur kisaran tahun, berturut-turut sebagai berikut; sangat mengerti (0%), mengerti (77,25%), kurang mengerti (22,75%) dan tidak mengerti (0,%) Secara umum, responden yang merumur tahun lebih mengerti, terhadap urgensi, walaupun perbedaannya tidak nyata.

Co-Authors Ade Agung Harnawan, Ade Agung Ade Saputra Adi Andrian Adi Rachmattulah, Adi Agustin, Dwina Laura Ahmad Rusadi Akbar, Arief Rahmad Maulana Akhmad Yusuf Alan Dwi Wibowo Arfan Eko Fahrudin Az Zahra, Aisyah Nur Budi Kristanto Cahya, Nila Dewi Sri Susanti Didin H Musrsyidin Dindin H Mursyidin Dwi Kartini Edi Mikrianto, Edi Mikrianto Elma, Tiara Faisal Faisal Fatma Indriani Hendy Setyoputro Heru Kartika Candra Hindarto, Imam Hisyam Musthafa, Hisyam Idris, Muddatstsir Irwan Budiman Iwan Sugriwan Iwan Sugriwan, Iwan Krisdianto Sugiyanto Lutfi Salisa Setiawati M Syahid Pebriadi Malisah, Siti Nor Manik, Tetti Novalina mariyati mariyati Muhammad Ahsar Karim Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi Nita Arianty Novita Meisida Nur Ridha Apriyanti Nur, Ailsa Dewinta Pardi Affandi Radityo Adi Nugroho Rahmat Eko Sanjaya Rahmat Yunus Rasjava, Achmad Ramadhanna'il RAUDLATUL MUNAWARAH Risky Meliawati Rudy Herteno Ryan Wahyudi Sigit Dwi Prabowo Siti Hatimah Rahmadaniah Siti Zubaidah Sri Cahyo Wahyono susi susi Susilo, Tanto Budi Sutomo Sutomo Thresye Thresye Thresye,, Thresye, Umi Baroroh Lili Utami Wahyu Ramadansyah Wiranto, Agung Setyo Yuni Yulida Yuyun Hidayat

Title

Found 30 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 30 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 30 Documents
Search