Article Per Year (5 Year)
p-Index From 2021 - 2026
2.17
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pseudocode Jurnal Penelitian Ekonomi dan Bisnis International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Advances: Jurnal Ekonomi & Bisnis
Edi Noersasongko
Faculty Of Economics And Business, Dian Nuswantoro University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 16 Documents
Search

 1   2 
PREDIKSI VOLUME LALU LINTAS ANGKUTAN LEBARAN PADA WILAYAH JAWA TENGAH DENGAN METODE K-MEANS CLUSTERING UNTUK ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) Evanita, Evanita; Noersasongko, Edi; Pramunendar, Ricardus Anggi
Jurnal Simetris Vol 7, No 1 (2016): JURNAL SIMETRIS VOLUME 7 NO 1 TAHUN 2016
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.085 KB)

Abstract

Di Indonesia kepadatan arus lalu lintas terjadi pada jam berangkat dan pulang kantor, hari-hari libur panjang atau hari-hari besar nasional terutama saat hari raya Idul Fitri (lebaran). Mudik sudah menjadi tradisi bagi masyarakat Indonesia yang ditunggu-tunggu menjelang lebaran, berbondong-bondong untuk pulang ke kampung halaman untuk bertemu dan berkumpul dengan keluarga. Kegiatan rutin tahunan ini banyak di lakukan khususnya bagi masyarakat kota-kota besar seperti Jakarta, dimana diketahui bahwa Jakarta adalah Ibu kota negara Republik Indonesia dan menjadi tujuan merantau untuk mencari pekerjaan yang lebih layak yang merupakan harapan besar bagi masyarakat desa. Volume kendaraan bertambah sejak 7 hari menjelang lebaran sampai 7 hari setelah lebaran tiap tahunnya terutama pada arah keluar dan masuk wilayah Jawa Tengah yang banyak menjadi tujuan mudik. Volume kendaraan saat arus mudik yang selalu meningkat inilah yang akan diteliti lebih lanjut dengan metode ANFIS agar dapat menjadi alternatif solusi  langkah  apa  yang  akan  dilakukan di  tahun  selanjutnya agar  pelayanan lalu  lintas, kemacetan panjang dan angka kecelakaan berkurang. Dengan input parameter ANFIS yang digunakan yaitu pengclusteran hingga 5 cluster, epoch 100, error goal 0 diperoleh performa terbaik ANFIS dengan K-Means clustering yang terbagi menjadi 3 cluster, epoch terbaik sebesar 20 dengan RMSE Training terbaik sebesar  0,1198,  RMSE  Testing terbaik sebesar  0,0282  dan  waktu proses tersingkat  sebesar 0,0695.Selanjutnya hasil prediksi diharapkan dapat bermanfaat menjadi alternatif solusi langkah apa yang akan dilakukan di tahun selanjutnya agar pelayanan lalu lintas lebih baik lagi.Kata kunci: angkutan lebaran, Jawa Tengah, ANFIS.
PREDIKSI VOLUME LALU LINTAS ANGKUTAN LEBARAN PADA WILAYAH JAWA TENGAH DENGAN METODE K-MEANS CLUSTERING UNTUK ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) Evanita Evanita; Edi Noersasongko; Ricardus Anggi Pramunendar
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 7, No 1 (2016): JURNAL SIMETRIS VOLUME 7 NO 1 TAHUN 2016
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.085 KB) | DOI: 10.24176/simet.v7i1.505

Abstract

Di Indonesia kepadatan arus lalu lintas terjadi pada jam berangkat dan pulang kantor, hari-hari libur panjang atau hari-hari besar nasional terutama saat hari raya Idul Fitri (lebaran). Mudik sudah menjadi tradisi bagi masyarakat Indonesia yang ditunggu-tunggu menjelang lebaran, berbondong-bondong untuk pulang ke kampung halaman untuk bertemu dan berkumpul dengan keluarga. Kegiatan rutin tahunan ini banyak di lakukan khususnya bagi masyarakat kota-kota besar seperti Jakarta, dimana diketahui bahwa Jakarta adalah Ibu kota negara Republik Indonesia dan menjadi tujuan merantau untuk mencari pekerjaan yang lebih layak yang merupakan harapan besar bagi masyarakat desa. Volume kendaraan bertambah sejak 7 hari menjelang lebaran sampai 7 hari setelah lebaran tiap tahunnya terutama pada arah keluar dan masuk wilayah Jawa Tengah yang banyak menjadi tujuan mudik. Volume kendaraan saat arus mudik yang selalu meningkat inilah yang akan diteliti lebih lanjut dengan metode ANFIS agar dapat menjadi alternatif solusi langkah apa yang akan dilakukan di tahun selanjutnya agar pelayanan lalu lintas, kemacetan panjang dan angka kecelakaan berkurang. Dengan input parameter ANFIS yang digunakan yaitu pengclusteran hingga 5 cluster, epoch 100, error goal 0 diperoleh performa terbaik ANFIS dengan K-Means clustering yang terbagi menjadi 3 cluster, epoch terbaik sebesar 20 dengan RMSE Training terbaik sebesar 0,1198, RMSE Testing terbaik sebesar 0,0282 dan waktu proses tersingkat sebesar 0,0695.Selanjutnya hasil prediksi diharapkan dapat bermanfaat menjadi alternatif solusi langkah apa yang akan dilakukan di tahun selanjutnya agar pelayanan lalu lintas lebih baik lagi.Kata kunci: angkutan lebaran, Jawa Tengah, ANFIS.
Pengaruh Peringkas Dokumen Otomatis Dengan Penggabungan Metode Fitur Dan Latent Semantic Analysis (LSA) Pada Proses Clustering Dokumen Teks Berbahasa Indonesia Muhammad Jamhari; Edi Noersasongko; Hendro Subagyo
Jurnal Pseudocode Vol 1, No 2 (2014)
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (7886.459 KB) | DOI: 10.33369/pseudocode.1.2.72-82

Abstract

Penyimpulan adalah proses pengumpulan bagian yang paling penting dari sebuah sumber dokumen yang menghasilkan versi yang lebih singkat. Metode yang dianggap paling layak untuk melakukan penyimpulan adalah metode berbasis fitur dan LSA (Latent Semantic Analysis). Pengklusteran adalah proses pengelompokan dokumen yang mempunyai kesamaan topik. Metode yang paling seringd ilakukan adalah LSA dimana SVD (Singular Value Decomposition) digunakan untuk menghubungkan semantik antara istilah dan kalimat begitu juga dengan dokumen. SVD juga mengurangi dimensi yang besar dari matriks dokumen istilah. Yang bersama dengan metode Feature Selection melakukan pengurangan fitur. Tesis ini memeriksa pengaruh metode penggabungan fitur dan metode LSA pada penyimpulan pada kumpulan data yang hasilnya akan diklusterkan berdasarkan pada LSA dimana SVD dilakukan bersamaan dengan metode seleksifitur. Uji coba yang dilakukan pada 150 dokumen dari 5 topik dengan beberapa kombinasi metode fitur metode LSA dan kedua metode digabungkan, pada tingkatan penyimpulan yang diintegrasikan tingkatan klusterisasi berdasarkan pada LSA dengan nilai k 12 dan metode kontribusi tema pemilih tema terbimbing memperlihatkan pengaruh yang besar pada metode yang digabungkan pada tahapan penyimpulan yang mendapatkan hasil akurasi 93.33%  dan waktu komputasi yang relatif cepat berkisar 57 detik dengan proporsi penggabungan seperti berikut : Kesimpulan LSA + 50% kesimpulan Fitur+20% seleksifitur+ Klusterisasi LSA.
The evaluation of convolutional neural network and genetic algorithm performance based on the number of hyperparameters for English handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Moch Arief Soeleman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1250-1259

Abstract

Convolutional neural network (CNN) has been widely applied to image recognition, especially handwritten English recognition. CNN's performance is good if the hyperparameter values are correct. However, the determination of precise hyperparameters is not a trivial task. This task is made more difficult when combined with a larger number of hyperparameters resulting in a high dimensionality of the search space. Usually, hyperparameter optimization uses a finite number. Previous studies have shown that a large number of hyperparameters can result in optimal CNN performance. However, the studies only apply to text mining datasets. This study offers two novelties. First, it applied 20 hyperparameters and their ranges to handwritten English. Second, this paper conducted seven experiments based on different hyperparameters and the number of hyperparameters. This paper also compares the existing methods, namely random and grid search. The experiment resulted in the proposed model being superior to the existing methods. EX3 is better than other experiments and a larger number of hyperparameters and layer-specific hyperparameter values are unimportant.
Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose Mula Agung Barata; Edi Noersasongko; Purwanto; Moch Arief Soeleman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4687

Abstract

Tea is one of the plantation products within the Ministry of Agriculture of the Republic of Indonesia, which plays an essential role as a mainstay commodity that boosts the Indonesian economy. Each type of tea has different properties, and the aroma of each type of tea can measure the quality of the tea. The human sense of smell is still very limited in classifying pure types of tea. Therefore, a device is needed to help measure the aroma of tea from an electronic nose. The devices attached to several gas sensors help humans take data from the smell of pure tea and calculate the value of each type of tea to test datasets with data mining algorithms. This study uses the C4.5 algorithm as a classification method with advantages over noise data, missing values, and handling variables with discrete and continuous types. Meanwhile, Chi-square is used to perform attribute severing in the data preprocessing process to increase the accuracy of dataset testing. Testing a pure tea dataset with four whole attributes, namely CO2, CO, H2, and CH4, using the C4.5 algorithm resulted in an accuracy of 93.65% and an increase in the accuracy performance of the C4.5 algorithm by 94.27% with dataset testing using Chi-Square feature selection with the two highest value attributes.
Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Mochammad Arief Soeleman
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.881

Abstract

Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.
Optimization of the Preprocessing Method for Edge Detection on Overlapping Cells at PAP Smear Images Nita Merlina; Edi Noersasongko; Pulung Nurtantio Andono; M Arief Soeleman; Dwiza Riana
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1329

Abstract

The complexity of the cell structure and high overlap cause poor image contrast. Complex imaging factors can make automatic visual interpretation more difficult. Segmentation separates a digital image into different parts with homogeneous attributes so that different areas have different features. The challenges faced in performing nucleus segmentation on Pap Smear (PS) images are poor contrast, the presence of neutrophils, and uneven staining of overlapping cells. This research was conducted to improve image quality in identifying the nucleus accurately. The method used is the Polynomial Contrast Enhancement (PCE) model as an approach to preprocessing. This method functions to change the contrast of the Pap smear image against the overlapping cells so that it becomes a significant contrast in detecting the edge of the nucleus object. The detection process uses the Robert and Prewitt edge detection method to test the identification of the nucleus object on 797 PS Repository images of the University of Nusa Mandiri (RepomedUNM). The accuracy result obtained is 86.8%. Comparing Robert's edge detection and Prewitt's edge detection shows that the PCE approach as a filter method can overcome color contrast problems and detect more accurately. The difficulty in detecting the nucleus from the PS image against the overlapping cells can be solved. This method can distinguish overlapping cells from their core during testing, thus becoming a reference in identifying cells with improved accuracy and testing larger data sets.
Gaussian Based-SMOTE Method for Handling Imbalanced Small Datasets Muhammad Misdram; Edi Noersasongko; Purwanto Purwanto; Muljono Muljono; Fandi Yulian Pamuji
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26881

Abstract

The problem of dataset imbalance needs special handling, because it often creates obstacles to the classification process. A very important problem in classification is to overcome a decrease in classification performance. There have been many published researches on the topic of overcoming dataset imbalances, but the results are still unsatisfactory. This is proven by the results of the average accuracy increase which is still not significant. There are several common methods that can be used to deal with dataset imbalances. For example, oversampling, undersampling, Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, Adasyn, Cluster-SMOTE methods. These methods in testing the results of the classification accuracy average are still relatively low. In this research the selected dataset is a medical dataset which is classified as a small dataset of less than 200 records. The proposed method is Gaussian Based-SMOTE which is expected to work in a normal distribution and can determine excess samples for minority classes. The Gaussian Based-SMOTE method is a contribution of this research and can produce better accuracy than the previous research. The way the Gaussian Based-SMOTE method works is to start by determining the random location of synthesis candidates, determining the Gaussian distribution. The results of these two methods are substituted to produce perfect synthetic values. Generated synthetic values are combined with SMOTE sampling of the majority data from the training data, produce balanced data. The result of the balanced data classification trial from the influence of the Gaussian Based SMOTE result in a significant increase in accuracy values of 3% on average.
Optimasi Centroid Awal Algoritma K-Medoids Menggunakan Particle Swarm Optimization Untuk Segmentasi Customer Wijaya, Danang Bagus; Noersasongko, Edi; Purwanto, Purwanto
Techno.Com Vol. 23 No. 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9516

Abstract

Customer segmentation is an important strategy in a company, it affects good customer relationships which will result in increased profits. Grouping customers in data mining can use several algorithms, but K-Medoids is the right choice because it can reduce noise and outlier sensitivity. However, the selection of cluster centers is still random and has an effect on the results of clustering, so it is necessary to improve the k-medoids algorithm so that the resulting cluster value can be optimal. Particle Swarm Optimization is an optimization algorithm that is often used and has been proven to improve the results of a clustering. In this case, optimization using Particle Swarm Optimization (PSO) in the selection of the initial cluster center needs to be applied to the k-medoids algorithm so that the results of the cluster can be optimal. The results of the study showed the Davies-Bouldin Index (DBI) value for K-Medoids K 2 = 0.379, K 3 = 0.283, and K 4 = 0.593, while the DBI value PSO + K-Medoids K 2 = 0.088, K 3 = 0.226, and K4 = 0.363. The DBI value shows that PSO optimization on K-Medoids to determine the initial centroid is proven to improve the results of clustering than standard K-Medoids.
Effectiveness of Individual Performance Dialogue on Employee Performance (Case Studies on Civil Servants of the Ministry of Finance in City of Semarang) Sudiarti, Ni Made Watimena; Noersasongko, Edi; Pramudi, Yuventius Tyas Catur
Jurnal Penelitian Ekonomi dan Bisnis Vol. 6 No. 1 (2021): March 2021
Publisher : Universitas Dian Nuswantoro Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jpeb.v6i1.4485

Abstract

Bureaucratic Reform in human resources began with the birth of Law No. 5 of 2014 on State Civil Apparatus (ASN). The new paradigm in the ASN Law makes ASN employees a profession that has an obligation to conduct self-development and must take responsibility for performance and apply merit principles in the implementation of ASN management. The Ministry of Finance followed up the Bureaucratic Reform in hr with performance management policies. Performance management in the Environment of the Ministry of Finance consists of three main stages, namely planning, monitoring, and determining performance results and evaluation.  Performance evaluation results determine the performance value of employees and organizations, which affects employee careers and compensation. The solution by the Ministry of Finance is to implement management practices called Organizational Performance Dialogue (DKO) and Individual Performance Dialogue (DKI) whose main purpose is to improve the performance of employees and organization. This research is explanatory, where the research method used is Partial Least Square (PLS), with software for analysis using SmartPLS. Data collection instruments using questionnaires and structured interviews with research respondents are employees of the Ministry of Finance in Semarang City area. This research aims to know the effectiveness of performance dialogue on employee performance, so that the results are expected to be a reference in management practices in organizational units in the environment of the Ministry of Finance in an effort to improve the performance of employees and organizations. Keywords:Effectiveness, Individual factors, Leadership factors, Performance dialogue, Employee performance
Co-Authors Affandy Alif Wahyu Oktaputra Dedi Dedi Dwi Jayanti Amanda Sari Dwiza Riana Evanita Evanita, Evanita Fandi Yulian Pamuji Fredy Susanto Happy Tatemba Harke Revo Leonard Polii Hendro Subagyo Jutono Gondohanindijo M Arief Soeleman Moch Arief Soeleman Mochammad Arief Soeleman Mohamad Jamhari Muhammad Misdram Muhammad Munsarif Mula Agung Barata Muljono, - Nita Merlina Nurtantio, Pulung Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Ricardus Anggi Pramunendar Rofiqi, Harri Kurniawan Sandra J. R Kainde Setiyani, Safira Hasna Soeleman, Arief Soeleman, M. Arief Sri Winarno Sudiarti, Ni Made Watimena Syamsiar, Syamsiar Wijaya, Danang Bagus Yuventius Tyas Catur Pramudi