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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) 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 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 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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Price Prediction Of Basic Material Using ARIMA Forecasting Method Through Open Data Sumedang District Kusnawi Kusnawi; M Andika Fadhil Eka Putra; Joang Ipmawati
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): 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.v12i2.2282

Abstract

In the era of Industry 4.0, characterized by the abundance of data, there are many opportunities to carry out various data-related processes. One of these is the data forecasting process which has been widely used. By analyzing data, we can make predictions and make decisions automatically. For example, one of the problems that decision-makers, especially in Kabupaten Sumedang, must solve is the changes in the prices of basic commodities that are essential for society's consumption. The prices of these commodities in the market tend to fluctuate in the short or long term. By analyzing the available data, we can predict the direction of changes in the prices of basic commodities in the market. In this study, the ARIMA model is used, which is one of the time series models that can be used to predict the possibility of an increase or decrease in the prices of basic commodities in the market in Kabupaten Sumedang. The ARIMA model uses the previous day's price data as a benchmark to predict the prices of basic commodities in the future. After being analyzed, the results of the model will be in several ARIMA model forms. An efficient ARIMA model will be used to model the prices of basic food commodities. This research produced the three best ARIMA models, namely ARIMA(1-1-1) for broiler chicken meat, ARIMA(0-1-1) for shallots, and ARIMA(0-1-1) for garlic. The accuracy test results percentage error for the best model using MAPE show an average value below 10%. Keywords: Food staples, Forecasting, Time Series, ARIMA, MAPE
OPTIMASI PENENTUAN SENTROID AWAL PADA K-MEANS UNTUK MENINGKATKAN HASIL EVALUASI DAVIES-BOULDIN INDEX Hendrik Hendrik; Kusrini Kusrini; Kusnawi Kusnawi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 1 (2024): EDISI 19
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i1.3873

Abstract

Perkembangan data yang semakin heterogen dan kompleks dengan volume yang terus meningkat menimbulkan kesulitan dalam pengolahan big data secara manual. Dalam konteks data mining, metode clustering menggunakan algoritma K-Means sering digunakan untuk mengelompokkan data berdasarkan kedekatan atau kemiripan. Namun, algoritma K-Means memiliki tantangan, terutama dalam menentukan jumlah cluster dan pemilihan sentroid awal yang dapat mempengaruhi hasil klasterisasi. Artikel ini membahas masalah tersebut dan mengusulkan dua metode optimasi, yaitu Elbow dan Particle Swarm Optimization (PSO), untuk meningkatkan hasil evaluasi algoritma K-Means. Dari hasil evaluasi, disimpulkan bahwa metode elbow lebih unggul dari segi waktu iterasi dibandingkan dengan metode PSO, meskipun keduanya menghasilkan nilai DBI yang sama. Waktu iterasi metode elbow hanya membutuhkan 0.297 detik, sedangkan metode PSO membutuhkan waktu iterasi selama 779 detik. Kesimpulannya, metode elbow lebih efisien dalam hal waktu iterasi. Saran untuk penelitian selanjutnya adalah menggunakan dataset yang berbeda dan mempertimbangkan metode optimasi lainnya untuk perbandingan evaluasi yang lebih komprehensif.
ANALISIS SENTIMEN REVIEW PENGGUNA APLIKASI FIZZO NOVEL DI GOOGLE PLAY MENGGUNAKAN ALGORITMA NAIVE BAYES Teguh Arlovin; Kusrini; Kusnawi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 6 No 1 (2024): EDISI 19
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v6i1.3909

Abstract

Perkembangan teknologi berdampak signifikan terhadap evolusi dunia digital, sesuai dengan kebutuhan praktis dan manfaat yang dirasakan masyarakat dari penggunaan teknologi sehari-hari. Dalam lingkup dunia digital, munculnya platform bacaan digital seperti Wattpad, Novelah, GoNovel, NovelFul, Inkitt, dan Fizzo Novel menandai salah satu perkembangan tersebut. Pentingnya analisis sentimen terhadap ulasan di platform ini menjadi krusial, mengingat analisis sentimen membantu mengidentifikasi kecenderungan opini dalam teks digital sebagai positif, negatif, atau netral. Penelitian ini bertujuan untuk mengklasifikasikan sentimen ulasan pada aplikasi Fizzo Novel menggunakan algoritma Naive Bayes dan mengevaluasi tingkat akurasinya. Fizzo Novel, sebagai aplikasi bacaan digital populer di tahun 2022, dipilih berkat jumlah ulasan pembaca yang variatif dan melimpah, menjadikannya kandidat ideal untuk analisis sentimen. Metodologi penelitian meliputi pengumpulan data, pra-pemrosesan data, dan pengembangan sistem dengan metode Naive Bayes, dengan data berupa ulasan pada platform Fizzo Novel selama Januari hingga Desember 2022. Hasil penelitian menunjukkan tingkat akurasi sebesar 83%, presisi 85%, recall 82%, dan spesifisitas 85%, menandakan kemampuan model dalam memprediksi sentimen secara akurat. F1 Score yang dihasilkan sebesar 83% menegaskan efektivitas model dalam mengklasifikasikan ulasan, menggarisbawahi pentingnya aplikasi analisis sentimen dalam meningkatkan pengalaman pengguna pada platform
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.
PERBANDINGAN SEGMENTASI CITRA SENI TARI PENDET DAN SENI BELA DIRI PENCAK SILAT: PENDEKATAN DENGAN MULTIRES UNET San Sudirman; Arief Setyanto; 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.
Penerapan Metode Moving Average untuk Memprediksi Stok Parfum Rizal Khadarusman; Kusrini; Kusnawi
bit-Tech Vol. 7 No. 1 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i1.1563

Abstract

Kekurangan stok parfum karena mengalami kesulitan dalam memprediksi kebutuhan stok untuk bulan berikutnya adalah hal yang tidak boleh terjadi. Proses pengadaan stok yang memerlukan waktu menambah tantangan ini, mengakibatkan dampak negatif pada omzet penjualan toko. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan metode data mining, khususnya teknik peramalan, untuk memprediksi kebutuhan stok parfum. Moving average dipilih karena kesederhanaan dan efektivitasnya dalam menangani data yang bersifat time-series. Metode ini menghitung rata-rata penjualan dalam periode waktu tertentu untuk memberikan estimasi kebutuhan stok di masa depan. Dengan pendekatan ini, kami dapat mengurangi dampak variabilitas data harian yang mungkin disebabkan oleh faktor musiman atau peristiwa tertentu, dan fokus pada tren yang lebih stabil. Dataset yang dianalisis mencakup data penjualan toko parfum dari Maret 2022 hingga September 2022, dan tiga parameter berbeda diuji dalam penelitian ini: periode 2 bulan, 3 bulan, dan 4 bulan. Hasil penelitian menunjukkan bahwa parameter 2 bulan memberikan prediksi paling akurat, dengan nilai error Mean Absolute Percentage Error (MAPE) sebesar 13,3%, menunjukkan tingkat akurasi yang baik. Kesimpulan dari penelitian ini adalah bahwa metode moving average dengan parameter 2 bulan efektif untuk memprediksi kebutuhan stok parfum, yang dapat membantu toko dalam mengelola persediaan dengan lebih efisien dan mengurangi risiko kekurangan stok. Temuan ini menunjukkan perlunya perencanaan persediaan yang lebih baik dan penggunaan metode prediksi yang tepat dalam industri parfum. Penelitian lebih lanjut mungkin diperlukan untuk mengoptimalkan pendekatan ini atau mengeksplorasi metode alternatif.
Penerapan Kombinasi Algoritma SVM-KNN dalam seleksi User SAKTI berdasarkan Hasil Kinerja Pegawai pada Kementerian XYZ Syaiful Ramadhan; 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.
Analisis Perbandingan Kinerja Algoritma Klasifikasi Pada Mahasiswa Berpotensi Dropout Tamuntuan, Virginia; Kusrini, Kusrini; Kusnawi, Kusnawi
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5658

Abstract

This research aims to compare the performance levels of two data mining classification algorithms, namely Support Vector Machine and Neural Network Backpropagation, using the K-fold cross-validation method. The data used consists of graduates from 2019 to 2023 at STMIK Multicom Bolaang Mongondow. A total of 80% of the 200 data points were used as training data, while the remaining 20% were used as testing data. K-fold cross-validation was conducted with K set to 5. The results of the study indicate that the Support Vector Machine algorithm achieved an accuracy of 80%, recall of 80%, and precision of 35%, while the Neural Network Backpropagation algorithm achieved an accuracy of 77%, recall of 63%, and precision of 44%.
Comparative Analysis of Long Short-Term Memory Architecture for Text Classification Fajar Abdillah, Moh; Kusnawi, Kusnawi
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1906.455-464

Abstract

Text classification which is a part of NLP is a grouping of objects in the form of text based on certain characteristics that show similarities between one document and another. One of methods used in text classification is LSTM. The performance of the LSTM method itself is influenced by several things such as datasets, architecture, and tools used to classify text. On this occasion, researchers analyse the effect of the number of layers in the LSTM architecture on the performance generated by the LSTM method. This research uses IMDB movie reviews data with a total of 50,000 data. The data consists of positive, negative data and there is data that does not yet have a label. IMDB Movie Reviews data go through several stages as follows: Data collection, data pre-processing, conversion to numerical format, text embedding using the pre-trained word embedding model: Fastext, train and test classification model using LSTM, finally validate and test the model so that the results are obtained from the stages of this research. The results of this study show that the one-layer LSTM architecture has the best accuracy compared to two-layer and three-layer LSTM with training accuracy and testing accuracy of one-layer LSTM which are 0.856 and 0.867. While the training accuracy and testing accuracy on two-layer LSTM are 0.846 and 0.854, the training accuracy and testing accuracy on three layers are 0.848 and 864.
Pengaruh Keseimbangan Data terhadap Akurasi Model Support Vector Machine pada Data Set Donor Darah Widyanto, Agung; Kusrini; Kusnawi
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.771

Abstract

In classification, unbalanced data is expected. Unbalanced data has an inequality ratio between the majority and minority classes. Models trained with unbalanced data tend to predict the minority class as the majority class. This study aims to determine the effect of data balance on the accuracy of the Support Vector Machine (SVM) classification model. The data set used is the blood donor data set downloaded from the repository belonging to the University of California, Irvine (UCI). The Waikato Environment for Knowledge Analysis (WEKA) tool was chosen to present the results of training development and model testing. The research framework scheme is used as a reference for knowledge flow. In scenario 1, data pre-processing includes handling missing values using mean-impulse and normalizing MinMax scaling. With a data set that has an inequality ratio of 1:3, the SVM classifier gets an accuracy performance of 76.7%. In scenario 2, post-pre-processing is done by balancing the data using the Synthetic Minority Oversampling Technique (SMOTE). SVM classifier gets 69.8% accuracy performance. Model performance is evaluated using confusion metrics. The gap in recall values for each class is very high in scenario 1 (2.8% and 99.8%). Things are different in scenario 2 (75.6% and 64%). The test results of 748 samples obtained an accuracy of 76.7% for the scenario-1 model and 93.2% for the scenario-2 model. This proves that the balance of data influences the accuracy of the SVM classification model.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Sanusi Mashuri Ahmad Yusuf Ainnur Rafli Ainul Yaqin Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Andi Sunyoto Anggit Dwi Hartanto, Anggit Dwi Arief Setyanto Arifuddin, Danang Arnila Sandi Aryawijaya Asadulloh, Bima Pramudya Assani, Moh. Yushi Atin Hasanah Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Cynthia Widodo Cynthia Widodo Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fachri Ardiansyah Fajar Abdillah, Moh Fajar Aji Prayoga Haris, Ruby Hartatik Haryo, Wasis Hasirun Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Indra Irawanto Joang Ipmawati Kanoena, Melcior Paitin Karisma Septa Kresna Khairullah, Irfan Khalil Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusirini Kusrini Kusrini Kusrini KUSRINI Kusrini - - Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Luthfi Nurul Huda M Andika Fadhil Eka Putra M. Nurul Wathani Maehendrayuga, Arief Majid Rahardi Maringka, Raissa Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Irvan Shandika Muhammad Reza Riansyah Nadhira Triadha Pitaloka Nayoma, Fisan Syafa Neni Firda Wardani Tan Ni’matur Rohim Nurul Zalza Bilal Jannah Nurus Sarifatul Ngaeni Omar Muhammad Altoumi Alsyaibani Pattimura, Yudha Bagas Pebri Antara Pramono, Aldi Yogie Prastyo, Rahmat Prema Adhitya Dharma Kusumah Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Rahayu, Christa Putri Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun Rizal Khadarusman Rodney Maringka Sabda Sastra Wangsa Saifulloh Saifulloh Salman Alfaris Salman Alfaris, Salman San Sudirman Sekarsih, Fitria Nuraini Sepriadi - Bumbungan Sepriadi Bumbungan Sri Yanto Qodarbaskoro Sry Faslia Hamka Suyatmi Suyatmi Suyatmi Suyatmi Syaiful Huda Syaiful Ramadhan Tamuntuan, Virginia Taryoko Taryoko Teguh Arlovin Thedjo Sentoso triadin, Yusrinnatul Jinana Van Daarten Pandiangan Virginia Tamuntuan Wahyu Pujiharto, Eka Widyanto, Agung Wirawan, Tegar Yusa, Aldo Yuza, Adela Zaenul Amri