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All Journal Jurnal Simetris JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jusikom : Jurnal Sistem Komputer Musirawas JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu JUSIM (Jurnal Sistem Informasi Musirawas) Jurnal Teknologi Komputer dan Sistem Informasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Digital Teknologi Informasi Jurnal Pengabdian kepada Masyarakat Nusantara Bulletin of Computer Science Research Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Teknologi Informasi Mura JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Jurnal UNIV.BI MENGABDI
Andri Anto Tri Susilo, Andri Anto
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A IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING UNTUK ANALISIS DATA NILAI AKADEMIK MAHASISWA Melizah, Melizah; Tri Susilo, Andri Anto; Lestari, Novi; Elmayati, Elmayati
Jurnal Teknologi Informasi Mura Vol 16 No 2 (2024): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v16i2.2427

Abstract

Data-data mahasiswa yang ada, apabila tidak diolah dengan baik dan benar, hanya akanmenjadi tumpukan data yang tidak berguna dan tidak bermanfaat. Padahal data tersebut bisadijadikan sebagai sumber data strategis yang sangat bermanfaat bagi Universitas Bina Insankhususnya Fakultas Ilmu Teknik. Dan juga Fakultas Ilmu Teknik Universitas Bina Insan belummemiliki suatu metode yang dapat digunakan untuk mengklasifikasikan nilai Indeks PrestasiKumulatif (IPK) mahasiswa. Proses klasifikasi banyak berperan dalam membantu proses analisadan hasil analisisnya dapat digunakan untuk memprediksi kinerja mahasiswa, dan mampumenghasilkan rekomendasi untuk pihak-pihak yang terkait di Universitas Bina Insan. Hasil dariklasifikasi tersebut diharapkan dapat memberikan alternatif pemecahan masalah yang dapatdikembangkan dalam suatu program yang dapat membantu mahasiswa yang memiliki masalahdengan indeks prestasinya. Penelitian ini menggunakan metode k-means clustering. DenganMetode K-Means Clustering diharapkan dapat mempermudah dalam mengklasifikasikan nilaiakademik mahasiswa berdasarkan nilai Indeks Prestasi Kumulatif (IPK). Dari pengujian modelyang dilakukan, didapatkan hasil yaitu nilai Silhouette Score adalah 0,80. Skor 0,80 menunjukkancluster yang bagus. Nilai Adjusted Rand Index (ARI) adalah 1, Nilai 1 menunjukkan keselarasansempurna antara dua klasterisasi. Nilai Adjusted Mutual Information (AMI) adalah 1, di mananilai 1 menunjukkan keselarasan sempurna antara dua klasterisasi
Penerapan Algoritma Apriori pada Pengolahan Data Transaksi Penjualan di Minimarket Priyo Kota Lubuklinggau Andri Anto Tri Susilo
JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi) Vol 1, No 3 (2018): JTKSI
Publisher : Institut Bakti Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jtksi.v1i3.657

Abstract

The development of mini-market in 2017 is extraordinary. If you ever heard the price of goods and many retail items. A most bona fide survey company, there is a change of shopping pattern from supermarket to minimarket, Dilubuklinggau, minimarket which can be classified into 2 that is minimarket owned by big company and minimarket owned by individual. In the process of data processing sales of goods, all minimarkets in Lubuklinggau City already using a database-based system. Ongoing sales transactions every time can lead to data collection, analysis used to generate information or information relevant to the minimarket. In addition, using this technique can also find patterns of products that are often used. To generate rules or rules between specific items for products that are technically used technical rules to find relationships between relations or items between sets. The end result of this study is the formation of several joint patterns of products sold that can be used by minimarket owners in the list of goods sold.
Metode Hybrid Dalam Pengelompokkan Kemampuan Calistung Siswa Berbasis Machine Learning Salsabila, Amanda; Andri Anto Tri Susilo; Nelly Khairani Daulay
Journal of Informatics Management and Information Technology Vol. 5 No. 2 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i2.500

Abstract

Students reading, writing, and arithmetic abilities (reading, writing, and arithmetic) are an important foundation in the academic development of elementary school students. This study aims to group students' reading, writing, and arithmetic abilities using a hybrid method based on machine learning, with grade data from two Elementary Schools in Lubuklinggau City. The method applied combines the K-Means Clustering algorithm for initial grouping and K-Nearest Neighbors (KNN) for classification. The analysis process includes data preprocessing, application of K-Means, cluster validation using Silhouette Score, and classification with KNN to ensure accuracy. As a result, K-Means successfully grouped students into three clusters: Middle (0), Low (1), and High (2). The KNN model with k = 3 which has the highest accuracy of 95% provides very good accuracy in testing the K-Nearest Neighbors (KNN) classification model with an accuracy of 97%, with very good precision, recall, and F1-score values for all clusters. These findings indicate that this hybrid approach is effective in classifying students' reading, writing and arithmetic abilities, which has implications for the development of more targeted learning strategies based on the characteristics of each group of students.
Pelatihan Microtik Untuk Meningkatkan Keterampilan Siswa Smk Negeri 5 Rejang Lebong Maya Sari, Wisdalia; Susilo, Andrianto Tri; Elmayati, Elmayati; Intan, Bunga; Santoso, Budi; Hidayat, Asep Toyib; Sobri, Ahmad; Aviani, Tri hasanah Bimastari; Wulandari, Cindi; Aprillian, Alif
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

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

Abstract

Jaringan komputer diera digitalisasi saat ini sangatlah penting, apalagi untuk siswa-siwa SMK jurusan teknik komputer jaringan. Untuk memudahkan para pelajar dalam pemakaian dan berbagi koneksi jaringan internet, maka dibutuhkan sebuah mikrotik router. Tentunya membagi-bagi koneksi internet harus memperhatikan beberapa hal dalam mengkonfigurasi mikrotik router. Sebagai solusi dari permasalahan tersebut, maka dibutuhkan pengetahuan dalam mengkonfigurasi mikrotik router. Adanya pelatihan jaringan ini diharapkan dapat memberi tambahan wawasan serta keterampilan bagi siswa SMKN 5 Rejang Lebong untuk menghadapi tantangan industri di masa mendatang. Kegiatan pengabdian ini menargetkan siswa-siswa SMK N 5 Rejang Lebong memiliki kompetensi yang seharusnya memang dimiliki oleh siswa-siswa SMK jurusan TKJ yang nantinya bisa menjadi kompetensi yang bisa dijadikan untuk mencari pekerjaan selain itu juga Pelatihan ini diharapkan bisa membuat para pelajar mengkonfigurasi mikrotik router dengan sempurna sehingga dapat dimanfaatkan oleh para pelajar yang membutuhkan koneksi internet dalam pembelajaran sehari-hari.
Pemanfaatan Metode Importance Performance Analisis Untuk Mengukur Tingkat Kepuasan Penguna Website Putra, Andriansah; Alamsyah, M. Nur; Susilo, Andrianto Tri
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5498

Abstract

The existence of technology has provided many benefits for human life, one of which is in the field of education, both universities and schools. One of the information system websites that are widely used in Indonesian society, the website is an important aspect in providing various information from various sources that can be accessed by the world community. Visitors assess the performance of a site on its ease of use and attractiveness. To determine the quality of the website, it is necessary to take measurements, these measurements need to be carried out to determine the quality of a website so that we can understand the opinions of users on a website whether users are satisfied or the quality of the website still needs to be improved. The implementation of this research using the Importance Peformance Analysis (IPA) method combines the measurement of the importance level factor (Performance) and the performance level (Importance) in a two-dimensional graph that facilitates data explanation and gets practical suggestions. After measuring the results of this study, this website needs to improve its performance so that the performance level is higher than the level of expectation (Importance).
Prediksi Harga Emas Mengunakan Jaringan Saraf Tiruan Algoritma Backpropagation Yupita Sari; Andri Anto Tri Susilo; Lukman Sunardi
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.566

Abstract

Gold is a precious metal with high value that is often used as an investment commodity due to its stability and tendency to increase in price compared to other assets, such as stocks. In the global economy, gold is also an important part of international reserves in national banks. However, public awareness of the benefits of gold investment remains low. One solution to increase interest and understanding of gold investment is to predict gold prices using accurate forecasting techniques. Forecasting utilizes historical data that is analyzed to project future trends, making it an important component in strategic decision-making. This study uses the backpropagation algorithm in artificial neural networks to predict gold prices. This algorithm minimizes errors in the data training process, improves model accuracy, and provides better results in prediction classification. Additionally, this algorithm is efficient in processing large amounts of training data, resulting in a reliable prediction model. The study aims to evaluate the performance of the backpropagation algorithm in predicting gold prices, including comparing the accuracy and correlation of predictions with other algorithms. The results of the study are expected to contribute to the development of a more accurate gold price prediction model, support investment decision-making, and increase public understanding of the benefits of investing in gold. This study successfully developed an Artificial Neural Network (ANN) model to predict gold futures prices based on historical data, including features such as opening price, high, low, and trading volume. The model was trained using the Backpropagation algorithm to capture non-linear patterns in complex data. The research results encompass three main aspects: Data Preprocessing, where data was effectively processed, including converting values to numerical format and normalizing features to accelerate model convergence; Model Training, where the model was trained using 80% of the training data and tested with 20% of the testing data; Monitoring train loss and validation loss shows that the model is learning well, although there are indications of overfitting risk. Evaluation and Prediction: The model is able to predict gold prices with good accuracy on the test data. Evaluation metrics such as MAE (Mean Absolute Error) show that the prediction results are quite close to the actual values, although there is still room for improvement. Overall, this model demonstrates satisfactory performance in predicting short-term gold prices and can be used as a tool in gold price analysis based on historical data.
IMPLEMENTASI ALGORITMA NAÏVE BAYES PADA KLASIFIKASI PENENTUAN JENIS KARTU KREDIT Tri Susilo, Andri Anto; Wijaya, Harma Oktafia Lingga; Elmayati, Elmayati
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 8 No 2 (2023): JUTIM (Jurnal Teknik Informatika Musirawas) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jutim.v1i1.2161

Abstract

Kartu Kredit adalah Alat Pembayaran Dengan Menggunakan Kartu (APMK) yang yang dikeluarkan oleh sebuah Bank dan dapat digunakan untuk melakukan pembayaran atas kewajiban yang timbul dari suatu kegiatan ekonomi, termasuk transaksi pembelanjaan dan/atau untuk melakukan penarikan tunai, dimana kewajiban pembayaran pemegang kartu dipenuhi terlebih dahulu oleh acquirer atau penerbit, dan pemegang kartu berkewajiban untuk melakukan pembayaran pada waktu yang disepakati baik dengan pelunasan secara sekaligus (charge card) ataupun dengan pembayaran secara angsuran. Dalam proses pengajuan kartu kredit yang dilakukan oleh nasabah, terdapat permasalahan yang timbul yaitu sulitnya analis kartu kredit dalam menentukan jenis kartu kredit yang sesuai untuk nasabah. Sulitnya analisa disebabkan karena banyak faktor pengikat didalam pemberian kartu kredit seperti jenis kelamin, status rumah, status, jumlah tanggungan, profesi, penghasilan per tahun dan yang lainnya. Data mining adalah proses pengumpulan dan pengolahan data yang bertujuan untuk mengekstrak informasi penting pada data. Proses pengumpulan dan ekstraksi informasi tersebut dapat dilakukan menggunakan perangkat lunak dengan bantuan perhitungan statistika, matematika, ataupun teknologi Artificial Intelligence (AI). Salah satu algoritma yang sering digunakan dalam proses data mining adalah Naïve Bayes Classifier. Naïve bayes clasiffier adalah pengklasifikasian statistik yang dapat digunakan untuk memprediksi probabilitas keanggotaan suatu class. Hasil penelitian berupa Implementasi Algoritma Naïve Bayes Pada Klasifikasi Penentuan Kartu Kredit. Dari pengujian model yang terbentuk dengan menggunakan persentase 80% data training dan 20 % data testing menggunakan algoritma naïve bayes, didapat nilai akurasi yaitu 0,975, Presisi yaitu 0,929, Nilai Recall yaitu 0,915 dan F1 Score 0,915. Kata Kunci :
PENGENALAN TEKNOLOGI INFORMASI DALAM FILM EDUKASI DI TANJUNG HARAPAN Lingga Wijaya, Harma Oktafia; Sari, Wisdalia Maya; Armanto, Armanto; Susilo, Andrianto Tri; Intan, Bunga; Nansyah, Satria
JURNAL UNIV.BI MENGABDI Vol 1 No 1 (2022): Jurnal UNIV.BI Mengabdi : Desember
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/mengabdi.v1i1.1879

Abstract

Film adalah salah satu hasil dari sebuah karya sastra yang dibuat oleh seseorang dengan imajinasi dan kreativitas yang dimilikinya. Film memuat cerita yang unik dan menarik sehingga banyak digemari oleh masyarakat karena sejak dulu keberadaan film diterima baik dalam kehidupan masyarakat. Film semakin populer karena menayangkan berbagai cerita yang mengandung nilai-nilai baik untuk diterapkan dalam kehidupan. Peranan media film dalam pembelajaran sangat penting untuk memberikan informasi terbaru mengenai perkembangan teknologi informasi. Pemanfaatan film dalam pembelajaran dapat dijadikan sebagai salah satu alternatif dalam mengarahkan anak-anak dan remaja setelah menyimak dan mengamati film yang dijadikan sebagai media informasi dan anak-anak dapat mengambil berbagai pelajaran yang positif terkait film tersebut.
A MODEL HIBRID RESNET-SVM UNTUK KLASIFIKASI PENYAKIT TANAMAN JAGUNG BERBASIS CITRA DAUN Tri Susilo, Andri Anto; Basri, Hasan; Kurniawan, Rudi
Jurnal Teknologi Informasi Mura Vol 17 No 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2744

Abstract

Abstrak Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) memberikan dampak signifikan dalam bidang pertanian, khususnya pada deteksi dan klasifikasi penyakit tanaman. Penelitian ini mengusulkan model hibrid yang mengintegrasikan Residual Network (ResNet) sebagai ekstraktor fitur dengan Support Vector Machine (SVM) sebagai classifier utama untuk mengklasifikasikan penyakit pada tanaman jagung berbasis citra daun. Dataset yang digunakan mencakup empat kelas, yaitu Blight, Common Rust, Gray Leaf Spot, serta daun jagung Healthy atau sehat. Hasil pengujian menunjukkan bahwa model hibrid ResNet-SVM mampu mencapai akurasi akhir sebesar 94,61%. Berdasarkan laporan klasifikasi, performa terbaik ditunjukkan pada kelas Healthy dengan nilai precision, recall, dan f1-score mencapai 1,00. Kelas Common Rust juga memperoleh hasil tinggi dengan f1-score 0,96, sedangkan kelas Blight mencapai f1-score 0,92. Namun, kelas Gray Leaf Spot masih menjadi tantangan dengan f1-score 0,62 akibat jumlah data yang relatif lebih sedikit. Secara keseluruhan, nilai macro average f1-score tercatat sebesar 0,88, sementara weighted average f1-score mencapai 0,94. Temuan ini menunjukkan bahwa kombinasi ResNet dan SVM efektif dalam meningkatkan akurasi klasifikasi penyakit jagung, sekaligus memperkuat potensi penerapan metode hibrid deep learning dan machine learning dalam sistem deteksi penyakit tanaman berbasis citra digital. Kata kunci: Resnet, SVM, Model Hibrid, Klasifikasi, Penyakit Jagung Abstract The advancement of Artificial Intelligence (AI) has significantly impacted agriculture, particularly in plant disease detection and classification. This study proposes a hybrid model that integrates Residual Network (ResNet) as a feature extractor with Support Vector Machine (SVM) as the main classifier for classifying corn leaf diseases based on image data. The dataset consists of four classes: Blight, Common Rust, Gray Leaf Spot, and Healthy leaves. Experimental results show that the hybrid ResNet-SVM model achieved a final accuracy of 94.61%. The best performance was obtained in the Healthy class with precision, recall, and f1-score of 1.00. The Common Rust class also achieved a high f1-score of 0.96, while the Blight class reached 0.92. However, the Gray Leaf Spot class remained more challenging, with an f1-score of 0.62 due to the relatively smaller number of samples. Overall, the model achieved a macro average f1-score of 0.88 and a weighted average f1-score of 0.94. These findings demonstrate that the combination of ResNet and SVM is effective in enhancing classification accuracy compared to single methods, highlighting its potential application in developing automated corn disease detection systems based on digital leaf images. Keywords: ResNet, SVM, hybrid model, classification, corn disease
Pengukuran Tingkat Kelembapan Tanah Dan Suhu Berbasis Arduino Uno pada Kelompok Tani Karya Maju II (Dua) Armanto Armanto; Andri Anto Tri Susilo; Harma Oktavia Lingga Wijaya; Wisdalia Maya Sari
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 4 (2022): Juni 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4197

Abstract

Electronic technology has recently developed rapidly, almost all aspects of daily human life have been covered by equipment with electronic technology systems, both using analog and digital control systems. Measurement is very important in science, especially in engineering. Measurement plays an important role in helping human work. As a country with vast natural resources, agriculture has enormous potential as state revenue. In addition, the agricultural sector is one of the most important sectors that increase the economic growth of the Indonesian people. One of the most important factors in agriculture is the quality of agricultural land. The better the agricultural land, the agricultural output will also increase. Factors that affect the quality of agricultural land are soil moisture and temperature. The life of biological elements contained in the soil including hosts, pathogens, and other microorganisms which vary greatly is influenced by soil moisture factors. condition of agricultural land in the area of Air Satan village. farmers in the air satan village have difficulty monitoring soil fertility in agricultural areas in the air satan village area, therefore the author wants to develop a tool that functions to measure the level of soil moisture with the measurement results displayed using a 16x2 LC which can be directly seen in order to make it easier for farmers or farmer groups in monitoring soil moisture and temperature in the agricultural area of Airsatan Village.
Co-Authors Anita Maharani Aprillian, Alif Armanto Armanto Armanto, Armanto aviani, tri hasanah bimastari Budi Santoso Bunga Intan Bunga Intan Dwi Budi Santoso Elmayati, Elmayati Harma Oktavia Lingga Wijaya Hasan Basri Hidayat, Asep Toyib Intan, Bunga Lestari, Novi Lingga Wijaya, Harma Oktafia Lukman Hakim Lukman Lukman Lukman Sunardi LW, Harma Oktavia Martadinata, A. Taqwa Maya Sari, Wisdalia Melizah, Melizah Mona Anggrelyka Putri Muhammad Cahaya Saputra nansyah, Satria Nelly Khairani Daulay Nur Alamsyah, Muhammad Putra, Andriansah Rohani, Dewi Rudi Kurniawan Salsabila, Amanda Sari, Wisdalia Maya sobri, ahmad Sunardi, Lukman Susanto Susanto Susanto Susanto Syamsul Arifin, M. Agus Veithzal Rivai Zainal Wijaya, Harma Oktavia Lingga Wisdalia Maya Sari Wisdalia Maya Sari Wiwit Lestari Wulandari, Cindi Yuliani Waruwu Yupita Sari