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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 Journal of Information Systems and Informatics Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, and 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 Bulletin of Network Engineer and Informatics (BUFNETS) 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
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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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.
INOVASI PENDIDIKAN MASA DEPAN DENGAN PEMANFAATAN TEKNOLOGI INFORMASI UNTUK MENINGKATAN KUALITAS PENDIDIKAN ANAK USIA DINI DI PKG PAUD GODEAN YOGYAKARTA Atmoko, Alfriadi Dwi; Yaqin, Ainul; Kusnawi
Jurnal Abdi Insani Vol 11 No 1 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i1.1199

Abstract

Pendidikan Anak Usia Dini (PAUD) in Indonesia is an important foundation for building a better future generation. PAUD in Indonesia is currently growing rapidly because awareness of the importance of education grows from an early age. Based on reference data from the Central Statistics Agency (BPS) and the Ministry of Education and Culture (Kemendikbud), the number of children attending PAUD in Indonesia will increase to 11.6 million in 2022, and the proportion of enrollment of children aged 4 to 6 years will be 74.3% . According to article 1.14 in the Law of the Republic of Indonesia No. 20 of 2003, PAUD education plays a very important role in the development of a child's personality and prepares them for the transition to the next level of education. PAUD implementation in Godean sub-district is under the Kapanewon Godean Yogyakarta Cluster Activity Center (PKG) where there are 61 members. In terms of implementing PAUD, it is hoped that the preparation of learning, monitoring and evaluation and management of learning activities can achieve good quality PAUD education, but in practice it has been supported by computers but not yet supported by information technology. The method of implementing this service is by providing training in Utilization of Google Workspace features and filling out online report cards for PAUD and FGD students.
Analisis Rekomendasi untuk Meningkatkan Nilai Capability Level Domain APO 14 Pada COBIT 2019 Taryoko, Taryoko; Muhammad, Alva Hendi; Kusnawi, Kusnawi
Jurnal Ilmiah Universitas Batanghari Jambi Vol 24, No 1 (2024): Februari
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v24i1.4380

Abstract

The purpose of this study was to determine data management with consideration of the APO 14 domain at XYZ Agencies using the 2019 COBIT Framework. This research method uses a case study. The results of this study indicate that first, the capability level test value is entered at level 3, namely Establish. Second, the average value generated on the Capability level test value is 3.14 or 0.031, which means the XYZ agency So that it can be ensured that the XYZ agency has carried out the implementation process and is able to achieve process results in accordance with what is targeted in the APO domain 14. Third, the average GAP value produced is worth 3 with a difference of 1 value from the expected value in accordance with the 2019 COBIT provisions.
Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia Kusnawi, Kusnawi; Rahardi, Majid; Pandiangan, Van Daarten
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1652

Abstract

Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.
MSME AI Readiness Analysis Using The AIRI Framework: Analisis Kesiapan AI UMKM Menggunakan Kerangka Kerja AIRI Muhammad Husein Budiraharjo; Alva Hendi Muhammad; Kusnawi
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.307

Abstract

AI is expected to become one of the key technologies supporting the development of MSMEs, which represent a major pillar of Indonesia's economy. Successful adoption and implementation of AI require the right strategies, one of which stems from an analysis of a company’s AI readiness. In this study, an AI readiness analysis was conducted using the AIRI framework on six MSMEs from various business sectors. The results of the analysis provided the AI readiness levels of each MSME, along with comparisons to similar industries and to industries of comparable business scale (MSME). The analysis also yielded several recommendations for AI adoption and strategies to enhance the AI readiness of each MSME. All the MSMEs involved in the study positively accepted the AI readiness analysis and the adoption recommendations provided. The study did not produce any feedback for improvements to the AIRI framework itself; however, there were suggestions for further development of the AIRI application to better assist MSMEs in determining AI readiness targets and appropriate AI implementation strategies in the future..
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Yusuf Ainnur Rafli Ainul Yaqin Aldi Yogie Pramono Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Ardiansyah, Fachri Arief Maehendrayuga 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 Christa Putri Rahayu 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 Hakiki, Muhammad Ridhwan Haris, Ruby Hartatik Haryo, Wasis Hasanah, Atin Hasirun Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Husni Hidayat Malik Indana Zulfa Indra Surya Permana Irawanto, Indra Joang Ipmawati Joang Ipmawati Joang Ipmawati Juventania Sheva Mellany Karisma Septa Kresna Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusrini Kusrini, Kusirini Ledyvia Audiz Coranov M Andika Fadhil Eka Putra M. Nurul Wathani Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Melcior Paitin Kanoena Mochamad Agung Wibowo Mochamad Agung Wibowo Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Husein Budiraharjo Muhammad Irvan Shandika Muhammad Irvan Shandika Muhammad Reza Riansyah Nayoma, Fisan Syafa Neni Firda Wardani Tan Ngaeni, Nurus Sarifatul Nurul Zalza Bilal Jannah Olajuwon, Sayyid Muh. Raziq Omar Muhammad Altoumi Alsyaibani Pandiangan, Van Daarten Pebri Antara Pitaloka, Nadhira Triadha Prastyo, Rahmat Pringandana, Cokorda Gde Lanang Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Raffa Nur Listiawan Dhito Eka Santoso RAMADHAN, SYAIFUL Ridwan Sanjaya Ridwan Sanjaya Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun RIYAN BAYU SATRIYA 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 Tegar Wirawan Teguh Arlovin Wahyu Pujiharto, Eka Wangsa, Sabda Sastra Wibowo , Mochamad Agung Widodo, Cynthia Widyanto, Agung Wirawan, Tegar Yudha Bagas Pattimura Yusa, Aldo Yusrinnatul Jinana triadin Yuza, Adela Zaenul Amri