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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa UG Journal Telematika Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Ilmiah KOMPUTASI JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang ILKOM Jurnal Ilmiah Jurnal Nasional Teknik Elektro dan Teknologi Informasi ICONESTH Jurnal Pengabdian Kepada Masyarakat Darma Saskara Prosiding SeNTIK STI&K
Rifki Kosasih
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Neuroscience Nursing Physics Public Health Social Sciences Other

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Perancangan Sistem Otomatis Transaksi Pembayaran Pada Marketplace UMKM Menggunakan Metode Crawling Horspool Rifki Kosasih; Eko Sri Margianti; Suryadi Harmanto; Didin Mukodim; Hendri Dwi Putra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4803

Abstract

UMKM MAPAN is one of the UMKM communities consisting of 300 UMKM entrepreneurs who are currently developing and located in the Depok area. These UMKM provide a wide variety of products to be marketed. In marketing these products, these UMKM group still uses the conventional method, is still by face-to-face with the buyer, so they have to rent a place first and are still limited in marketing online. This method has many weaknesses, such as the high cost of renting a place and the difficulty of finding a strategic location to market the product. Therefore, in this study, an e-commerce marketplace web system was created that could accommodate 300 UMKM MAPAN entrepreneurs in marketing their products. In addition, an automatic system for payment transactions on the UMKM marketplace was also created using the Horspool crawling method so that it could make it easier for UMKM entrepreneurs to print payment transaction reports. Based on the research results, the success rate of report printing is 100%. In this study, the complexity of the Horspool algorithm is O(n) with n is length of pattern while the time complexity of the Horspool algorithm is O(m+σ) with m is length of search string.
Pendeteksian Kendaraan Menggunakan Metode Median Filter: Array Rifki Kosasih
Jurnal Ilmiah Komputasi Vol. 20 No. 1 (2021): Jurnal Ilmiah Komputasi Volume: 20 No. 1, Maret 2021
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.20.1.402

Abstract

Untuk mengetahui keadaan lalu lintas sekitar biasanya pihak keamanan menggunakan video CCTV. Akan tetapi, pengawasan dengan menggunakan kamera CCTV tidak mungkin dilakukan sepanjang waktu. Oleh karena itu dibutuhkan suatu metode yang dapat membantu pengawasan lalu lintas seperti mendeteksi kendaraan-kendaraan yang lewat. Pada penelitian ini digunakan metode Median Filter dan tresholding untuk mendeteksi kendaraan yang lewat. Data yang digunakan adalah video lalu lintas kendaraan di jalan tol Bintara Bekasi. Setelah itu, video tersebut diubah menjadi kumpulan frame-frame citra dan hitung selisih antara frame terbaru dengan sebelumnya. Tahapan selanjutnya adalah menggunakan metode Median Filter adalah untuk menghilangkan noise pada citra hasil selisih frame tersebut. Setelah itu dilakukan tresholding untuk mendapatkan objek kendaraan yang diinginkan yang disebut dengan citra foreground. Tahapan terakhir adalah memuat bounding box pada objek kendaraan yang telah terdeteksi. Berdasarkan hasil penelitian diperoleh kendaraan terdeteksi di kondisi nyata dan dinyatakan sebagai kendaraan di aplikasi sebanyak 34 kendaraan, kendaraan terdeteksi di kondisi nyata tetapi tidak dinyatakan aplikasi diperoleh sebanyak 5 dan tidak ada kendaraan terdeteksi dalam kondisi nyata namun dinyatakan kendaraan di aplikasi diperoleh sebanyak 4. Berdasarkan hasil tersebut diperoleh nilai presisi sebesar 87,1794%, nilai recall sebesar 89,4736% dan tingkat akurasi sebesar 79,0697%.
Travel Time Estimation Using Support Vector Regression on Model with 8 Features Kosasih, Rifki; Mardhiyah, Iffatul
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.37215

Abstract

Purpose: In travelling, we need to predict travel time so that itinerary is as expected. This paper proposes Support Vector Regression (SVR) to build a prediction model. In this case, we will estimate travel time in the Bali area. We propose to use a regression model with 8 features, i.e., time, weather, route, wind speed, day, precipitation, temperature and humidity information.Methods: In this study, we collect real-time data from Global Positioning System (GPS) and weather applications. We divide our data into two types: training dataset consisting of 177 data and testing dataset comprising 51 data. The Support Vector Regression (SVR) method is used in the training stage to build a model representing data. To validate the model, error measurements were carried out by calculating the values of R2, Accuracy, MAE (Mean Absolute Error), RMSE (Root Mean Square Error) and Accuracy.Result: From the research results, the model obtained is the SVR model with parameters γ=0.125, ε=0.1 and C = 1000, which has a value of R2= 0.9860528612283006. Later, we predict travel times on testing data using the SVR model that has been obtained. Based on the result of the research, our model has a 0.8008 MAE (Mean Absolute Error), 1.2817 RMSE (Root Mean Square Error) and 95.3369% Accuracy.Novelty: In this study, we use 8 features to estimate travel time in the Bali area. Furthermore, we will compare the KNN regression method (previous studies) with Support Vector Regression (SVR) (proposed method) on a model with 8 features to estimate travel time.
Identification of mangrove tree species using deep learning method Paranita Asnur; Rifki Kosasih; Sarifuddin Madenda; Dewi A. Rahayu
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp163-170

Abstract

Artificial intelligence can help classify plants to make identification easier for everyone. This technology can be used to classify mangrove trees. The degradation of mangrove forests has resulted in a 20% loss of biodiversity, an 80% loss of microbial decomposers, reduced C-organic soil, and fish spawning grounds, resulting in estimated losses in the ecological and economic fields for up to IDR 39 billion. The identification of different mangrove species is the first step in ensuring the preservation of these forests. Therefore, this research aimed to develop algorithms and a convolutional neural network (CNN) architecture to classify mangrove tree species with the highest possible accuracy using Python software. The architecture selection for this model includes a batch size of 32, an input image size of 128x128 pixels, four classes, four convolution layers, four rectified linear unit (ReLU) layers, 2x2 max-pooling, and two fully connected layers (FCL). The finding showed that the resulting accuracy from the test was 97.50%, while the validation test was 81.25%, applied to four types of mangrove leaves, including Avicenia marina, Avicenia officialis, Rizophora apiculata, and Soneratia caseolaris.
Implementation of K Nearest Neighbor in Detecting Heart Disease with Various Training Data Rifki Kosasih; Iffatul Mardhiyah
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 2 (2023): July 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i2.44303

Abstract

Salah satu organ penting dalam tubuh manusia adalah jantung, Jika jantung mengalami gangguan maka dapat menyebabkan penyakit jantung. Untuk mendeteksi adanya penyakit jantung biasanya dilakukan dengan berkonsultasi dengan tenaga medis. Akan tetapi dengan semakin banyaknya pasien di rumah sakit akan dapat memperlambat pendeteksian penyakit jantung. Oleh karena itu dibutuhkan suatu sistem yang dapat membantu tenaga medis dalam mempercepat pendeteksian penyakit jantung. Dalam penelitian ini diusulkan untuk menggunakan pendekatan machine learning seperti metode K Nearest Neighbor (KNN) dalam mendeteksi penyakit jantung. Data yang digunakan sebanyak 1025 pasien dengan 13 fitur seperti umur, jenis kelamin, rasa sakit di dada, tekanan darah saat sedang istirahat, kadar kolesterol, gula darah, hasil elektrografik saat sedang istirahat, detak jantung maksimal, jika mengalami nyeri dada saat latihan, depresi yang diinduksi oleh latihan relatif, kemiringan puncak ST segmen, jumlah pembuluh darah yang berwarna setelah diwarnai flourosopy dan tipe kerusakan pembuluh darah. Pada penelitian ini dilakukan tiga skema pembagian data latih dan data uji dengan rasio 60:40, 70:30 dan 80:20. Berdasarkan hasil pengujian diperoleh bahwa tingkat akurasi, presisi dan recall tertinggi terjadi Ketika rasio data latih dan data uji 70:30 yaitu sebesar 97,0779% untuk akurasi, 97,9166% untuk presisi dan 95,9183% untuk recall.One of the important organs in humans is the heart. If the heart is disturbed, it can cause heart disease. To detect the presence of heart disease is usually done in consultation with doctor. However, with the increasing number of patients in the hospital, it will be able to slow down the detection of heart disease. Therefore, we need a system that can assist doctors in accelerating the detection of heart disease. In this study, we propose to use a machine learning approach i.e., K Nearest Neighbor (KNN) method in detecting heart disease. The data used were 1025 patients with 13 features i.e., age, gender, chest pain, blood pressure, cholesterol, blood sugar, electrographic results, maximum heart rate, if you experience chest pain during exercise, depression which exercise-induced relative, peak slope, number of blood vessels after fluoroscopy and type of vessel damage. In this study, we have three schemes in divide training data and test data with ratios of 60:40, 70:30 and 80:20. Based on the test results, it was found that the highest levels of accuracy, precision and recall occurred when the ratio of training data and test data was 70:30, which was 97.0779% for accuracy, 97,9166 for precision and 95,9183% for recall.
Face Recognition Using Isomap, KNN and Naïve Bayes Classifier Rifki Kosasih
CogITo Smart Journal Vol. 9 No. 1 (2023): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v9i1.473.38-47

Abstract

Sistem pengenalan wajah merupakan sistem yang dapat mengenali wajah seseorang dengan bantuan komputer. Untuk mengenali wajah tersebut, dilakukan ekstraksi fitur wajah. Pada penelitian ini digunakan metode isomap untuk mengekstrak fitur wajah. Isomap merupakan suatu metode yang dapat mengubah dimensi citra dari tinggi menjadi fitur-fitur yang memiliki dimensi rendah. Data yang digunakan adalah citra wajah yang diperoleh dari 6 orang, setiap orang memiliki 4 variasi ekspresi citra. Setelah fitur wajah diekstrak, selanjutnya dilakukan klasifikasi dengan menggunakan metode K Nearest Neighbor (KNN) dan metode Naive Bayes Classifier. KNN merupakan metode klasifikasi yang menggunakan jumlah tetangga (K) terdekat untuk menentukan kelas sedangkan Naïve Bayes merupakan metode klasifikasi yang menggunakan peluang bersyarat untuk menentukan kelas. Berdasarkan hasil penelitian pada metode KNN, tingkat akurasi terbaik terjadi saat jumlah tetangga K = 2. Nilai akurasi yang diperoleh sebesar 87,5%, nilai rata-rata presisi terbobot (RPT) sebesar 81,25% dan nilai rata- rata recall terbobot (RRT) sebesar 87,5% Pada metode Naive Bayes Classifier diperoleh tingkat akurasi sebesar 50%, nilai rata-rata presisi terbobot (RPT) sebesar 62% dan nilai rata-rata recall terbobot (RRT) sebesar 50%. Berdasarkan hasil tersebut dapat disimpulkan bahwa metode KNN merupakan metode klasifikasi yang lebih akurat dan lebih baik dibandingkan dengan metode Naïve Bayes.
PENINGKATAN KAPASITAS SISWA DAN ORANGTUA DI RW.22 PERUMMETLAND CILEUNGSI SEKTOR VII: MATEMATIKA DAN PELATIHAN GOOGLE MEET Mardhiyah, Iffatul; Talita, Aini Suri; Indarti, Dina; Kosasih, Rifki; Fazriyati, Elyna; Hutapea, Octaviani
Jurnal Pengabdian Kepada Masyarakat Darma Saskara Vol 3, No 1 (2023)
Publisher : Jurnal Pengabdian Kepada Masyarakat Darma Saskara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/abdimasug.2023.v3i1.10021

Abstract

Pengabdian masyarakat dilakukan di RW 22 Perum Metland Cileungsi Sektor VII untuk meningkatkan pemahaman matematika siswa dan kemampuan penggunaan Google Meet pada ibu-ibu. Kegiatan ini diilustrasikan oleh rendahnya pemahaman konsep matematika dan kurangnya penguasaan teknologi oleh ibu-ibu. Dalam empat bulan, kegiatan melibatkan30 siswa dan 20 ibu-ibu, menggunakan metode pengajaran matematika kontekstual dan media visual, serta pelatihan penggunaan Google Meet dengan pendekatan hands-on. Evaluasi melibatkan kuesioner, wawancara, dan tes hasil belajar. Hasil menunjukkan peningkatan pemahaman matematika, kecakapan penggunaan Google Meet, serta penerapan teknologi dalam pembelajaran matematika. Pelatihan dan modul pembelajaran matematika telah berhasilmeningkatkan daya saing siswa dan ibu-ibu. Pengabdian ini membuktikan pentingnya penerapan iptek dalam pendidikan dan memberikan rekomendasi kebijakan untuk pengembangan sumber belajar masyarakat. Dengan ini, kemampuan masyarakat dalam menghadapi era digital dapat ditingkatkan.
Predicting levels of legal case difficulties using machine learning Sari, Ilmiyati; Kosasih, Rifki; Indarti, Dina
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4364-4371

Abstract

Lawyers play a crucial role in the courtroom, assisting clients in their defense. Because of their lack of legal expertise, a person or organization facing legal issues requires professional aid. However, we need to know how much money will be spent on paying lawyers. The level of complexity in a case can be used to determine lawyer costs. Therefore, in this research, we propose employing machine learning methodologies, i.e., random forest classifiers and support vector machines (SVM), to determine the level of legal case difficulties. The novelty of this research is applying a machine learning approach in predicting the level of difficulty of legal cases. The data utilized consists of 990 records, which are divided into training and testing data in a 90:10 ratio. The term frequency-inverse document frequency (TF-IDF) approach was then utilized to perform text preprocessing. The text-preprocessing findings are utilized as input in the classification process. According to the research findings, an accuracy value of 85%, a value of weighted average precision is 88%, and a value of weighted average recall is 85%, for support vector machine. Using random forest, we achieve an accuracy value of 89%, a value of weighted average precision is 85.6%, and a value of weighted average recall is 80%.
IMPLEMENTATION OF SUPPORT VECTOR REGRESSION FOR PREDICTING LAWYER CHARGES USING CLOUD COMPUTING IN GOOGLE COLAB Ilmiyati Sari; Rifki Kosasih; Dina Indarti
International Conference on Education, Science, Technology and Health (ICONESTH) 2023: ICONESTH
Publisher : International Conference on Education, Science, Technology and Health (ICONESTH)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46244/iconesth.vi.246

Abstract

In court, the role of the lawyer is needed by the defendant in carrying out the defense. In various legal cases, lawyers are able to assist and accompany their clients until the judge's decision is rendered. However, lawyer charge can vary depending on several factors. This research proposes 3 factors that can affect attorney fees. i.e., length of sentence, type of case and distance of lawyer's office to Jakarta district court. The data used in this study is 100 judge's decision data originating from the website of the Jakarta district court. Then the data is divided into two parts with 80 data for training data and 20 for test data. In this study, we predict the lawyer charge by implementing Support Vector Regression (SVR) method in Google Colab. Based on results of this research, we found that the value of Mean Square Error (MSE) was 0.401, the value of Mean Absolute Error (MAE) was 0.347, and the value of Root Mean Square Error (RMSE) was 0.633.
PERBANDINGAN KLASIFIKASI KERUSAKAN JALAN MODEL CNN VGG19 DAN RESNET50 Revanza Raditya Putra Yanni; Iffatul Mardhiyah; Dyah Cita Irawati; Rifki Kosasih; Dyan Prawita Sari
Jurnal Ilmiah Informatika Komputer Vol 30, No 1 (2025)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2025.v30i1.14230

Abstract

Masalah kerusakan jalan pada jalan utama adalah salah satu gangguan saat berkendara dan dapat menyebabkan kecelakaan. Identifikasi kerusakan jalan masih dilakukan secara manual oleh pemerintah daerah dengan penyisiran jalan. Penggunaan teknologi kecerdasan buatan untuk identifikasi kerusakan jalan, sangat diperlukan. Algoritma CNN dapat melakukan identifikasi dan klasifikasi kerusakan jalan. Beberapa arsitektur pada CNN yang sering digunakan untuk klasifikasi diantaranya VGG19 dan ResNet50. Penelitian ini bertujuan untuk mengetahui perbandingan klasifikasi antara VGG19 dan ResNet50 pada kerusakan jalan. Perbandingan dilakukan dengan membedakan jumlah epochnya untuk setiap arsitektur. Jumlah dataset yang digunakan sebanyak 1656 citra. Model yang dibentuk bertujuan mengklasifikasikan kerusakan jalan menjadi tiga klasifikasi yaitu, kerusakan_besar, kerusakan_sedang, dan kerusakan_kecil. Jumlah epoch yang digunakan pada model adalah sebesar 10, 50, dan 100. Hasil dari penelitian arsitektur VGG19 dengan epoch 10 mendapatkan akurasi sebesar 79%, epoch 50 sebesar 73%, dan epoch 100 sebesar 76%. Arsitektur ResNet50 memperoleh hasil akurasi sebesar 75% dengan epoch 10, untuk epoch 50 sebesar 78%, dan epoch 100 sebesar 79%. Kesimpulan penelitian perbandingan klasifikasi kerusakan jalan, VGG19 dapat mengklasifikasikan lebih baik jika proses pelatihan yang lebih sederhana, sedangkan ResNet50 dapat melakukan klasifikasi lebih baik jika proses pelatihan yang lebih kompleks.
Co-Authors Achmad Fahrurozi Ahmad Sabri Aini Suri Talita Anggi Alberto Christian Daomara Desti Riminarsih Dewi Agushinta R. Dewi Putrie Lestari Didin Mukodim Dina Indarti Dyah Cita Irawati Dyan Prawita Sari Eko Sri Margianti Fadlia, Nur Fahrurozi, Achmad Fazriyati, Elyna Genoveva, Zahwa Hendri Dwi Putra Hidayat Taufik Akbar Hutapea, Octaviani Iffatul Mardhiyah Ilmiyati Sari Ilmiyati Sari Irawati, Dyah Cita Mardhiyah, Iffatul Muhammad Arfiansyah Nurabsharina, Amanda Putri Nurma Nugraha Rahayu, Dicka Ariptian Revanza Raditya Putra Yanni Sari, Dyan Prawita Sarifuddin Madenda SUDARYANTO SUDARYANTO Suryadi Harmanto