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All Journal Bulletin of Electrical Engineering and Informatics Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Universitas Batanghari Jambi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Aisyah Journal of Informatics and Electrical Engineering Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Respati Jurnal Abdi Insani JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Computer System and Informatics (JoSYC) Jurnal Graha Pengabdian Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration TEPIAN Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA SENADA : Semangat Nasional Dalam MengabdI Journal of Electrical Engineering and Computer (JEECOM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sisfotek Global Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia SENADA : Semangat Nasional Dalam Mengabdi TECHNOVATAR Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Jurnal Teknik AMATA Jurnal TAM (Technology Acceptance Model)
Kusnawi Kusnawi
Universitas Amikom Yogyakarta
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Peningkatan Akurasi Deteksi Kendaraan Menggunakan Kombinasi Haar Cascade Classifier dan Convolutional Neural Networks (CNN) Irawanto, Indra; Sunyoto, Andi; Kusnawi, Kusnawi
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 1 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i1.8242

Abstract

Teknologi pengolahan citra digital dan computer vision telah memainkan peran penting dalam meningkatkan sistem pengaturan lalu lintas. Meskipun kamera CCTV umum digunakan, kebanyakan sistem masih bersifat pasif dan terbatas dalam pengawasan arus lalu lintas. Dalam menanggapi kebutuhan akan sistem yang lebih proaktif dan adaptif, dikembangkan berbagai sistem Manajemen Lalu Lintas Pintar yang mengintegrasikan teknologi deteksi objek kendaraan canggih, seperti kombinasi Haar Cascade Classifier dengan Convolutional Neural Network (CNN). Haar Cascade Classifier efektif dalam mendeteksi objek real-time, namun dapat mengalami kesulitan dalam kondisi gambar kompleks. Integrasi dengan CNN diharapkan meningkatkan akurasi deteksi kendaraan dalam berbagai kondisi pencahayaan dan latar belakang. Penelitian ini bertujuan untuk mengeksplorasi arsitektur CNN yang optimal untuk diintegrasikan dengan Haar Cascade guna mencapai efisiensi dan akurasi deteksi kendaraan yang lebih tinggi dalam pengaturan lalu lintas. Dari hasil eksperimen, kombinasi Haar Cascade dan CNN efektif dalam mendeteksi dan mengestimasi jumlah kendaraan. Performa model tergantung pada kompleksitas gambar, di mana semakin kompleks gambar, semakin rendah akurasi dan sensitivitasnya. Penggunaan arsitektur MobileNet dan Xception menunjukkan kemampuan yang baik dalam mendeteksi kendaraan, dengan Xception memberikan sedikit peningkatan dalam akurasi (80.13%) dibandingkan dengan MobileNet (79.19%), namun dengan waktu komputasi yang sedikit lebih lama (1.02 detik dibandingkan dengan 0.82 detik). Pilihan antara kedua model tergantung pada kebutuhan spesifik aplikasi, seperti kebutuhan untuk akurasi yang lebih tinggi atau kecepatan pemrosesan yang lebih cepat. Dengan demikian, penelitian ini berpotensi untuk memberikan kontribusi signifikan bagi pengembangan sistem lalu lintas yang lebih cerdas dan responsif di masa depan.
Analisis Kombinasi Algoritma K-Means Clustering dan TOPSIS Untuk Menentukan Pendekatan Strategi Marketing Berdasarkan Background Target Audiens Ngaeni, Nurus Sarifatul; Kusrini, Kusrini; Kusnawi, Kusnawi
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4948

Abstract

The promotion is an annual agenda for STIMIK Tunas Bangsa Banjarnegara. The aim of this promotional activity is to attract more new students every year. On the other hand, campus promotion encounters obstacles in mapping applicant data from previous years so that considerations for new promotion policies are based on data from the school of origin of alumni or students. By using the K-Means Clustering algorithm, applicant data can be grouped according to the background represented through the school origin attribute. , parents' occupation and place of origin. Then the data is processed using DSS with the TOPSIS method to obtain priority references for marketing types for each cluster. The results of calculating the silhouette coefficient value for the five clusters obtained a score of 0.426. Meanwhile, in the ranking process using the TOPSIS method, the first rank was found in cluster 0 with a score of 0.994110. Further stages use the Decision Tree method to obtain output in the form of recommendations for promotion types for each cluster. For example, cluster 0 is recommended to use promotion types with codes P1, P2, P3, P8 and P9.
PCOS DISEASE CLASSIFICATION USING FEATURE SELECTION RFECV AND EDA WITH KNN ALGORITHM METHOD Pitaloka, Nadhira Triadha; Kusnawi, Kusnawi
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.831

Abstract

Polycystic ovary syndrome is an endocrine disorder of the ovaries that causes hormonal disturbances in women of reproductive age, where androgen secretion in the ovaries of women with Polycystic Ovary Syndrome (PCOS) is excessive compared to normal women. This usually occur in women with obesity which is characterized by irregular menstrual cycles, chronic anovulation, hyperandrogenism, and even infertility. Efforts are used to treat this disease in the form of hormone therapy, laparoscopic ovarian drilling, and in-vitro fertilization. However, these three therapies are focused on symptomatic therapy and are less effective in treating PCOS-related infertility. Detecting PCOS disease early is very necessary so that prevention and treatment can be carried out immediately. Therefore, a classification is carried out to detect PCOS disease by being able to analyze data that has a high degree of accuracy. The method used for the classification of PCOS disease is using the K Nearest Neighbor (KNN), method which previously carried out the feature selection process, namely the Exploratory Data Analysis (EDA), method which is used for the data analysis process by means of an analysis approach to data to find out the most accurate method and using the Recursive Feature Elimination and Cross-Validation (RFECV) selection method which ranks the features based on their level of importance to the prediction process. Further, the data classification process uses the K-Nearest Neighbors (KNN) algorithm. The results of the Exploratory Data Analysis (EDA) feature selection process produce 10 data attributes that are used and are continued by the Recursive Feature Elimination and Cross-Validation (RFECV) process by producing the 7 most important attributes used and finally the K-Nearest Neighbors (KNN) method has a high level high accuracy by producing an accuracy value of 93%, precision 82%, recall 100%, and F1 score 90%.
COMPARISON OF LEAST SQUARE AND QUADRATIC METHODS ON PREDICTION THE NUMBER OF NEW STUDENT APPLICANTS Atin Hasanah; Kusrini, Kusrini; Kusnawi, Kusnawi
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1124

Abstract

New student registration is held every year with several mechanisms. However, in recent years the number of applicants has decreased even though it had experienced a surge in the previous year. So that, it is necessary to have a prediction to predict the number of applicants in the coming year. In addition, the results of these predictions can be used as material for consideration in determining the quota/ceiling for the number of new student admissions in the following academic year. This research used the Least Square and Quadratic methods to predict the number of new student applicants based on data on the number of applicants from the 2014/2015 to 2022/2023 academic years. Performance testing of the two methods was tested with three (3) testing methods : MAE, MAPE, and MSE. The performance test found that the Quadratic method is more suitable with the MAPE value in the "Good" forecasting accuracy category, which is 11%. For the MAE value, it gets 452,17 and an MSE of 302069,04. While Least Square produces a MAPE value in the "Enough" forecasting accuracy category of 30%, for the MAE value, it gets 996,97 and an MSE of 1494205,36.
Comparison ff Sentiment Labeling Using Textblob, Vader, and Flair in Public Opinion Analysis Post-2024 Presidential Inauguration with IndoBERT Kusnawi, Kusnawi; Anam, Khoerul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4015

Abstract

The results of the 2024 Indonesian presidential election decided that Prabowo Subianto and Gibran Rakabuming Raka became the elected pair of Indonesian presidential and vice-presidential candidates in 2024. The pair's election triggered various public reactions, especially on social media platforms. Some social media platforms provided diverse opinions, indicating a wide variety of views on this issue. This research aims to analyze public opinion after the election of the 2024 Indonesian president by comparing sentiment using TextBlob, VADER (Valence Aware Dictionary and sEntiment Reasoner), and Flair. Training and testing are done with the IndoBERT model to determine the most effective sentiment labeling. This research starts by collecting text data from social media X, YouTube, and Instagram, then preprocessing, translating, and labeling data using three libraries, training, and testing using IndoBERT. The results of training and testing data show that Flair has an accuracy of 81.29%, TextBlob has an accuracy of 73.35%, and VADER has an accuracy of 74.86%. From the accuracy results obtained, it can be concluded that labeling using Flair provides the greatest accuracy of the others because the Flair labeling process uses deep learning and contextual embedding techniques.
Penerapan Kombinasi Algoritma SVM-KNN dalam seleksi User SAKTI berdasarkan Hasil Kinerja Pegawai pada Kementerian XYZ Ramadhan, Syaiful; Kusrini, Kusrini; Kusnawi, Kusnawi
Jurnal Teknologi Informatika dan Komputer Vol. 9 No. 2 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i2.1716

Abstract

Kementerian XYZ merupakan Kementerian dengan jumlah pegawai lebih dari 5.000 pegawai. Pada saat dibentuk tidak dilakukan pemetaan pegawai, hal ini mengakibatkan surplus jumlah pegawai, tidak terkecuali pada Biro Barang Milik Negara (BMN). Bagi sebuah organisasi, SDM yang berlimpah merupakan hal yang baik, namun perlu dilakukan penyeleksian pegawai agar dapat meningkatkan produktivitas sehingga keberhasilan organisasi dapat tercapai. Disamping itu, perbaikan sistem Administrasi Keuangan pemerintahan merupakan suatu keharusan yang diimbangi dengan pengembangan aplikasi terintegrasi Kementerian Keuangan yaitu Sistem Aplikasi Keuangan Tingkat Instansi (SAKTI). Dalam melakukan pengelolaan aset pada Biro BMN, setiap pegawai memiliki role user level kewenangan SAKTI dengan lingkup yang berbeda-beda. Penelitian ini bertujuan melakukan seleksi klasifikasi user berdasarkan hasil penilaian kinerja dengan penerapan metode Kombinasi algoritma SVM dan KNN menggunakan bahasa pemrograman Python. Berdasarkan pengujian dengan sampel data sebesar ±313 data pegawai dan 18 variabel pegawai dengan atribut target berupa kelayakan yaitu dipertahankan maupun dipertimbangkan, diperoleh hasil akurasi sebesar 94% pada Kernel SVM RBF; nilai K=5; metrik Euclidean;  Dapat disimpulkan seleksi user aplikasi SAKTI menggunakan kombinasi algoritma SVM dan KNN dapat memberikan prediksi guna meningkatkan efektivitas dan efisiensi organisasi dalam penempatan pegawai yang sesuai dengan kompetensi pada Biro BMN Kementerian XYZ. Penelitian selanjutnya diharapkan dapat membandingkan kombinasi algoritma SVM dan KNN dengan metrik serta parameter yang lebih banyak.
Identification of Lumpy Skin Disease in Cattle with Image Classification using the Convolutional Neural Network Method Sentoso, Thedjo; Ardiansyah, Fachri; Tamuntuan, Virginia; Wangsa, Sabda Sastra; Kusrini, Kusrini; Kusnawi, Kusnawi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.2569

Abstract

One of the problems often faced by cattle farmers is related to diseases in their cattle where one of the cattle diseases whose transmission rate is very fast is Lumpy Skin Disease (LSD). Currently, to identify the health of livestock, especially in cattle, is still very dependent on experts and of course this takes time, resulting in delays in the prevention and treatment of diseases in cattle, especially this LSD disease. The Convolutional Neural Network (CNN) algorithm is one of the algorithms can used for image classification of cows whether the cow is healthy or Lumpy. The stages of this research start from problem identification, literature study, data collection, algorithm implementation, testing, and performance evaluation results of the algorithm on cattle disease data. In this research, testing was conducted using three architectures for CNN: VGG16, VGG19, and ResNet50. The results of the experiment showed that VGG16 was the most effective architecture compared to VGG19 and ResNet50, with a training accuracy of 95.31% and a loss value of 0.1292, as well as a testing accuracy of 96.88% and a loss value of 0.102.
Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine: Sentiment Analysis of Tourist Attractions Based on Reviews on Google Maps Using the Support Vector Machine Algorithm Ipmawati, Joang; Saifulloh, Saifulloh; Kusnawi, Kusnawi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1066

Abstract

Era Revolusi Industri 4.0 ditandai oleh ketersediaan data yang melimpah, menciptakan peluang dalam proses pengambilan informasi. Salah satu proses pengambilan data tersebut mencakup pencarian informasi tentang tempat wisata di Yogyakarta (DIY). Proses pengambilan informasi ini dapat dilakukan melalui Google Maps, yang menyediakan detail seperti lokasi, jarak, bahkan ulasan pengunjung dalam bagian komentar, yang berasal dari ulasan tentang destinasi wisata tersebut. Dalam data informasi yang dikumpulkan, muncul berbagai masalah yang memerlukan identifikasi, mengarah pada gagasan penelitian untuk menganalisis sentimen terkait destinasi wisata dengan memanfaatkan ulasan pengguna di Google Maps. Metodologi penelitian yang digunakan dalam studi ini menggunakan Support Vector Machine (SVM) untuk mengategorikan ulasan ke dalam kategori sentimen positif, negatif, atau netral. Ulasan pengguna dari platform Google Maps diolah dan dilatih menggunakan SVM untuk mengidentifikasi pola sentimen. Hasil eksperimen menunjukkan efektivitas metode SVM dalam mengelola volume besar data ulasan untuk analisis sentimen, memberikan pemahaman yang lebih dalam tentang persepsi masyarakat terhadap destinasi wisata. Penelitian ini dapat berkontribusi pada pengembangan strategi pemasaran dan manajemen berdasarkan umpan balik pengguna secara real-time. Temuan penelitian mengenai kinerja metode SVM dalam klasifikasi analisis sentimen menggunakan Support Vector Machine (SVM) menunjukkan tingkat akurasi rata-rata sebesar 83,8% berdasarkan ulasan pengunjung di situs Google Maps.
PERBANDINGAN SEGMENTASI CITRA SENI TARI PENDET DAN SENI BELA DIRI PENCAK SILAT: PENDEKATAN DENGAN MULTIRES UNET Sudirman, San; Setyanto, Arief; Kusnawi, Kusnawi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4331

Abstract

This research compares image segmentation of the Pendet dance art and the Pencak Silat martial art using the MultiRes U-Net approach. Research methods include data collection, data pre-processing, data sharing, evaluation, and results. Evaluation results using the Dice coefficient, Jaccard index, and Mean Squared Error (MSE) metrics show the best scores for each dataset. The results of this research can increase understanding of these two arts and cultures through deeper visual analysis. The results of the image segmentation evaluation between Pendet dance and Pencak Silat martial arts using the MultiRes UNET approach show the best scores for Dice Coefficient (DC), Jaccard index, and Mean Squared Error (MSE). The best scores for the Pendet dance dataset are 98.47, 99.23, and 8.20E-04, while for the Pencak Silat dataset they are 88.29, 85.98, and 4.52E-04. Evaluation shows a good level of similarity between the segmented image and the original image.
Design of Automatic Feeder with Adjustable Temperature, PH, and Weather for Catfish Antara, Pebri; Utami, Ema; Kusnawi, Kusnawi
Cerdika: Jurnal Ilmiah Indonesia Vol. 5 No. 4 (2025): Cerdika: Jurnal Ilmiah Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/cerdika.v5i4.2473

Abstract

Catfish is a fish that is consumed by many Indonesians and is one of the business opportunities. This can be a good prospect for the future with catfish farming. Even many restaurants and restaurants use this fish as one of the menus. Cultivating catfish is not easy, there are many parameters that need to be considered so that fish life can develop properly. Starting from sufficient water ph or according to the standard of living catfish, up to the air temperature. These factors will affect if catfish farms are ignored. Timely feeding is also a major factor that can have sufficient nutrition. With an automatic fish feed system and water quality monitoring, farmers' problems related to water quality can be monitored using sensors. Feeding can be done via a telegram bot with certain settings as needed.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Yusuf Ainnur Rafli Ainul Yaqin Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Anggit Dwi Hartanto, Anggit Dwi Ardiansyah, Fachri Arief Setyanto Arifuddin, Danang Arnila Sandi Aryawijaya Asadulloh, Bima Pramudya Assani, Moh. Yushi Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dharma Kusumah, Prema Adhitya Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fajar Abdillah, Moh Fajar Aji Prayoga Haris, Ruby Hartatik Haryo, Wasis Hasanah, Atin Hasirun Hasirun Hasirun, Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Irawanto, Indra Joang Ipmawati Kanoena, Melcior Paitin Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusrini Kusrini, Kusirini M Andika Fadhil Eka Putra M. Nurul Wathani Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Mochamad Agung Wibowo Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Husein Budiraharjo Muhammad Irvan Shandika Muhammad Reza Riansyah Nayoma, Fisan Syafa Neni Firda Wardani Tan Ngaeni, Nurus Sarifatul Nurul Zalza Bilal Jannah Omar Muhammad Altoumi Alsyaibani Pandiangan, Van Daarten Pattimura, Yudha Bagas Pebri Antara Pitaloka, Nadhira Triadha Pramono, Aldi Yogie Prastyo, Rahmat Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Rahayu, Christa Putri RAMADHAN, SYAIFUL Ridwan Sanjaya Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun Rizal Khadarusman Rodney Maringka Rohim, Ni’matur saifulloh Saifulloh, saifulloh Salman Alfaris Salman Alfaris, Salman San Sudirman Sekarsih, Fitria Nuraini Sentoso, Thedjo Sepriadi - Bumbungan Sepriadi Bumbungan Sri Yanto Qodarbaskoro Sry Faslia Hamka Sudirman, San Suyatmi Suyatmi Suyatmi Suyatmi Syaiful Huda Syaiful Ramadhan Tamuntuan, Virginia Taryoko, Taryoko Teguh Arlovin Wahyu Pujiharto, Eka Wangsa, Sabda Sastra Widodo, Cynthia Widyanto, Agung Wirawan, Tegar Yusa, Aldo Yusrinnatul Jinana triadin Yuza, Adela Zaenul Amri