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All Journal TEKNIK INFORMATIKA Jurnal Simantec Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Informatika dan Teknik Elektro Terapan Jurnal IPTEK Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Telematika Jurnal Teknologi Terpadu Jurnal Teknik Informatika (JUTIF) International Journal of Artificial Intelligence and Robotics (IJAIR) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer
Muchamad Kurniawan, Muchamad
Institut Teknologi Adhi Tama Surabaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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SEGMENTASI CITRA WAJAH DENGAN MENGGUNAKAN METODE K-MEANS – L*A*B Fawaz, Ahmad; Hakimah, Maftahatul; Kurniawan, Muchamad
Prosiding Seminar Nasional Sains dan Teknologi Terapan 2021: Peluang dan Tantangan Peningkatan Riset dan Teknologi di Era Pasca Covid-19
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Segmentasi adalah salah satu teknik yang digunakan untuk memisahkan antara object dengan background. Objek yang digunakan dalam penelitian ini adalah objek wajah manusia. Dengan segmentasi, citra wajah manusia dapat terpisah dengan backgroundnya. Teknik segmentasi yang digunakan adalah metode clustering k-means. K-means merupakan salah satu algoritma yang dapat menyelesaikan masalah clustering, selain dengan metode k-means dibutuhkan juga proses perpindahan dari citra yang diambil berupa warna RGB menjadi warna L*a*b. Ruang warna L*a* b merupakan sebuah ruang warna yang terdiri dari tiga nilai numerik,yaitu L* untuk level cahaya dan a*  b* itu untuk komponen hijau-merah dan biru kuning. Keberagaman background pada suatu citra wajah merupakan sebuah tantangan tersendiri dalam melakukan proses pemisahan wajah yang menggunakan metode k-means. Pengambilan citra wajah dilakukan dengan 2 tempat yaitu ruangan dalam (indoor) dan luar ruangan (outdoor). Hasil akurasi terbaik didalam ruangan (indoor) sebesar 99,64% dan citra diluar ruangan (outdoor) sebesar 99,29%
Review Pemanfataan Data Electroencephalogram (EEG) dengan metode Convolution Neural Network Kurniawan, Muchamad; Rachman, Andy; Pakarbudi, Adib
INTEGER: Journal of Information Technology Vol 6, No 2: September 2021
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.0.v6i2.2419

Abstract

Electroencephalogram (EEG) is a brain data signal that is captured by sensors. Many studies have used EEG to be used as a decision maker or classifying. What classification has been used most frequently in existing studies over the last 5 years? These are the questions that will be answered in this research. In addition to these questions, another question that will be answered is what is the most popular method used in processing EEG data? The final question in research is the recent development of EEG and CNN research. The results of these answers are the most popular research using the CNN method as a classification method, the application of the field of Human-computer InterfacesKeywords: Electroencephalogram, Convolution Neural Network.  
Analisis Fast Moving Consumer Goods untuk Memprakirakan Penjualan Barang Menggunakan Metode Triple Exponential Smoothing Ar, Nanda Hafiz; Kurniawan, Muchamad
INTEGER: Journal of Information Technology Vol 6, No 2: September 2021
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.0.v6i2.2311

Abstract

Fast Moving Consumer Goods (FMCG) refers to a business sector generating economy particularly in Indonesia. The movement of goods runs quickly as they belong to staple food and have relatively short shelf life. They are sometimes unpredictable and even out of stock specifically to goods in fast moving category. Consequently, business doers can lose opportunities. Therefore, sale prediction is necessary to reduce opportunity loss and stock piling upon the goods that should not be ordered excessively. This research conducted prediction through Triple Exponential Smoothing method in the period of January 2018 to June 2020 by taking 5 item samples that were then tried out using alpha 0.1 – 0.9. As a result, alpha 0.1 became the best alpha in this research compared to alpha 0.2 – 0.9. Out of 5 trials, alpha 0.1 (MAPE 22%, 19%, and 34%) occurred three times and alpha 0.2 (MAPE 34% and 11%) happened twice. However, this research has not obtained the best result yet as it has not satisfied the indicator of more than 10% whole MAPEs. Thus, Triple Exponential Smoothing Brown was less appropriate to the data being used. The calculation of estimation did not consider the data fluctuation such as Ramadhan event greatly affecting the data training and forecasting result
A LOF K-Means Clustering on Hotspot Data Muhima, Rani Rotul; Kurniawan, Muchamad; Pambudi, Oktavian Tegar
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 1 (2020): IJAIR : May
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.964 KB) | DOI: 10.25139/ijair.v2i1.2634

Abstract

K-Means is the most popular of clustering method, but its drawback is sensitivity to outliers. This paper discusses the addition of the outlier removal method to the K-Means method to improve the performance of clustering. The outlier removal method was added to the Local Outlier Factor (LOF). LOF is the representative outlier’s detection algorithm based on density. In this research, the method is called LOF K-Means. The first applying clustering by using the K-Means method on hotspot data and then finding outliers using the LOF method.  The object detected outliers are then removed.  Then new centroid for each group is obtained using the K-Means method again. This dataset was taken from the FIRM are provided by the National Aeronautics and Space Administration (NASA).  Clustering was done by varying the number of clusters (k = 10, 15, 20, 25, 30, 35, 40, 45 and 50) with cluster optimal is k = 20. The result based on the value of Sum of Squared Error (SSE) shown the LOF K-Means method was better than the K-Means method. 
Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset Kurniawan, Muchamad; Muhima, Rani Rotul; Agustini, Siti
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 2 No. 2 (2020): IJAIR : November
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2292.074 KB) | DOI: 10.25139/ijair.v2i2.3030

Abstract

One of the causes of forest fires is the lack of speed of handling when a fire occurs. This can be anticipated by determining how many extinguishing units are in the center of the hot spot. To get hotspots, NASA has provided an active fire dataset. The clustering method is used to get the most optimal centroid point. The clustering methods we use are K-Means, Fuzzy C-Means (FCM), and Average Linkage. The reason for using K-means is a simple method and has been applied in various areas. FCM is a partition-based clustering algorithm which is a development of the K-means method. The hierarchical based clustering method is represented by the Average Linkage method.  The measurement technique that uses is the sum of the internal distance of each cluster. Elbow evaluation is used to evaluate the optimal cluster. The results obtained after conducting the K-Means trial obtained the best results with a total distance of 145.35 km, and the best clusters from this method were 4 clusters. Meanwhile, the total distance values obtained from the FCM and Linkage methods were 154.13 km and 266.61 km.
Improvement Of Query Speaking on The Indonesian to Madura Dictionary Using Levenshtein Distance Method Ubaidillah, M. Yahya; Kurniawan, Muchamad; Rosetya Wardhana, Septiyawan
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 3 No. 2 (2021): IJAIR : November
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v3i2.4258

Abstract

Men are distinguished from other living beings by their use of language, which becomes one of their most distinctive and humanistic qualities. Many different languages are spoken worldwide, including Indonesian, which has approximately 742 different dialects. Due to the unique language of Madura, which is located on a large island with numerous beach tourism destinations, tourists will have difficulty navigating the island. People outside Madura Island who come to visit or vacation will find it difficult to communicate with the locals during their stay or holiday. An Indonesian to Madurese translation dictionary is therefore required in this case. The Levenshtein Distance method was employed in this investigation. The algorithm in the dictionary is used to process the search for the closest distance (dif) between the words being inputted and the words that are already in the database. To provide a prototype for the use of dictionaries. Indonesian and Madurese data sets were used in the investigation by the researcher. According to the simulation results acquired after multiple trials, the error accuracy was 90 % for the first letter input, 84 % for the middle letter input, and 84 % for the last letter input for the first letter. As a result, according to the study's findings, the accuracy of this dictionary increased by 86 %. The first letter received 90 % of the votes, the middle letter received 84 %, and the last letter received 84 %. As a result, according to the study's findings, the accuracy of this dictionary increased by 86 %. The first letter received 90 % of the votes, the middle letter received 84 %, and the last letter received 84 %. As a result, according to the study's findings, the accuracy of this dictionary increased by 86 %.
RUTE TERPENDEK ALGORITMA PARTICLE SWARM OPTIMIZATION DAN BRUTE FORCE UNTUK OPTIMASI TRAVELLING SALESMAN PROBLEM Muchamad Kurniawan; Farida Farida; Siti Agustini
JURNAL TEKNIK INFORMATIKA Vol 14, No 2 (2021): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v14i2.19094

Abstract

Distribution becomes an important measure of marketing success. Traveling Salesman Problem is an example of a case that can be implemented in a distribution case study to get the shortest route through which a distributor passes. The distributor must pass each node (address or city) once in a while and then return to the node where he started. Traveling salesman problems emerge as part of logistical and transportation problems that have developed and utilized in the current period which is growing in various sectors. This research proposes using the Particle Swarm Optimization and Brute Force method to compare the performance of the two methods to get the shortest route. The study was conducted in several experiments the number of points (nodes) namely 5, 10, 15, 20, 25, and 30 nodes. Overall experiments, the Particle Swarm Optimization algorithm is superior to Brute Force. The route produced by Particle Swarm Optimization has a shorter distance than Brute Force
ANALISIS FITUR HAAR MENGGUNAKAN ALGORITMA HAAR-LIKE FEATURE PADA CITRA KENDARAAN BERMOTOR Nabila Dayu Mega Anjani; Farida Farida; Muchamad Kurniawan
Network Engineering Research Operation Vol 5, No 2 (2020): NERO
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v5i2.187

Abstract

Jenis kendaraan bermotor terdiri dari sepeda motor, mobil, bus dan truk. Setiap jenis kendaraan bermotor memiliki ciri-ciri khusus yang digunakan sebagai pembeda. Pengenalan objek kendaraan berdasarkan jenis dan teknik pengolahan citra telah banyak dikembangkan oleh beberapa peneliti dengan menggabungkan metode deteksi kendaraan salah satunya metode haar like feature. Penelitian ini mencari filter yang tepat untuk digunakan pada proses deteksi kendaraan bermotor. Beberapa proses haar like feature yang dilakukan diantaranya integral image, haar training, haar testing dan labeling. Berdasarkan hasil pengujian pemilihan filter pada proses haar training memperoleh tipe filter (1,2) dapat mengenali 4 objek kendaraan mobil dan 0 objek bis dengan hasil akurasi 80%. Sedangkan tipe filter (2,2) dapat mengenali 1 objek kendaraan mobil dan 0 kendaraan bis dengan hasil akurasi 71%. Pada proses haar testing memperoleh tipe filter (1,2) dapat mengenali 2 kendaraan mobil dan 1 kendaraan bis dengan hasil akurasi 88,8%. Sedangkan tipe filter (2,2) dapat mengenali 3 objek kendaraan mobil dan 1 objek kendaraan bis dengan hasil akurasi 90%.
Penerapan Algoritma CT-Pro untuk Mengetahui Pola Pembelian Konsumen (Pada Studi Kasus Toko Bahan Kue H2R Surabaya) Muhammad Aditya Kushardiawan; Maftahatul Hakimah; Muchamad Kurniawan
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2022: SNESTIK II
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.477 KB) | DOI: 10.31284/p.snestik.2022.2813

Abstract

Dalam beberapa tahun terakhir, peminat bahan kue meningkat sangat tinggi. Oleh karena itu, pihak manajemen dari toko terus mengembangkan strategi peningkatan penjualan dan pemasaran produk yang lebih baik. Kemampuan bertahan bisnis sangat bergantung pada kemampuan dalam memahami konsumen dan proses pengambilan keputusan konsumen dari hari ke hari. Masalah tersebut dapat diatasi dengan melakukan analisis data transaksi serta mengasosiasikan data transaksi. Tujuan dari penelitian ini adalah bagaimana mengetahui informasi tentang pola pembelian konsumen dan dapat memberikan rekomendasi penempatan produk kepada pemilik toko dengan cara menempatkan berbagai produk di dalam satu tempat yang berdekatan. Manfaat dari penelitian ini yakni tersedianya sebuah sistem yang dapat mengetahui pola pembelian konsumen bagi pemilik toko dan memudahkan pemilik toko ketika transaksi pembelian. Dari analisis dan hasil uji coba yang sudah dilakukan menggunakan metode asosiasi dengan algoritma CT-Pro untuk mengetahui pola pembelian konsumen pada Toko H2R, dapat disimpulkan bahwa pola kombinasi tertinggi adalah produk Cakra dan Segitiga dengan nilai confidence 3% dan produk Cakra dan Gogo dengan nilai confidence 2,2%. Semakin  banyak jumlah data yang digunakan dalam penelitian, semakin tinggi nilai minimal support. Akan tetapi, jumlah kemunculan pola pembelian suatu produk akan lebih sedikit.
Perbandingan SVM dan Perceptron dengan Optimasi Heuristik Kurniawan, Muchamad; Hakimah, Maftahatul; Agustini, Siti
Jurnal Telematika Vol. 15 No. 2 (2020)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v15i2.356

Abstract

Support Vector Machine (SVM) and Perceptron are methods used in machine learning to determine classification. Both methods have the same motivation, namely to get the dividing line (hyperplane). Hyperplane can be obtained by using the optimization method Gradient Descent (GD), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). This study compares machine learning methods (Support Vector Machine and Perceptron) to optimization methods (Gradient Descent, Genetic Algorithm, and Particle Swarm Optimization) to find hyperplane. The dataset used is Iris Flower obtained from the UCI Machine Learning Repository. The test parameter on the Perceptron is the learning rate, while the optimization algorithm (GA and PSO) is the number of individuals. The results showed that the most suitable optimization method for Perceptron and SVM is PSO, with an accuracy value of 93%. Support Vector Machine (SVM) dan Perceptron merupakan metode yang digunakan dalam machine learning untuk penentuan klasifikasi. Kedua metode tersebut memiliki motivasi yang sama, yaitu untuk mendapatkan garis pemisah (hyperplane). Hyperplane bisa didapatkan dengan metode optimasi Gradient Descent (GD), Genetic Algorithm (GA), dan Particle Swarm Optimization (PSO). Penelitian ini membandingkan metode machine learning (Support Vector Machine dan Perceptron) terhadap metode optimasi (Gradient Descent, Genetic Algorithm, dan Particle Swarm Optimization) untuk menemukan hyperplane. Dataset yang digunakan adalah Iris Flower yang diperoleh dari UCI Machine Learning Repository. Parameter pengujian pada Perceptron adalah learning rate, sedangkan pada algoritme optimasi (GA dan PSO) adalah jumlah individu. Hasil penelitian menunjukkan bahwa metode optimasi yang paling cocok untuk Perceptron dan SVM adalah PSO, dengan nilai akurasi 93%.
Co-Authors Abdullah, Ryan Gading Andy Rachman Ar, Nanda Hafiz Arum Indah Sari Fahrezi Kusuma, Andra Farida Farida Farida Farida Faruq, Umar Al Fawaz, Ahmad Gusti Eka Yuliastuti Hadimarta, Tommy Ferdian Hendro Nugroho, Hendro Ilmi, Hilmi Maulana Istighfariansyah, Risaldi Maftahatul Hakimah, Maftahatul Misbahul Munir Moch. Kalam Mollah Moneter, Mochamad Rizky Muhammad Aditya Kushardiawan Muhima, Rani Rotul Nabila Dayu Mega Anjani Naili Saidatin Nanik Suciati Nurlaily, Firdausiyah O.T., Pambudi Pakarbudi, Adib Pambudi, Oktavian Tegar Pratikto, Dimas Priska Amelia de Jong Ritonga, Mario Franko Ezra Hasiholan Rosetya Wardhana, Septiyawan Setyawati, Indah Siti Agustini, Siti Ubaidillah, M. Yahya Wardhana, Septiyawan Rosetya