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All Journal Jurnal TIMES MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sistem Informasi Kaputama (JSIK) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) INCODING: Journal of Informatics and Computer Science Engineering Journal of Mathematics and Technology (MATECH) Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik, Komputer, Agroteknologi dan Sains
Sembiring, Arnes
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Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Transportation

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PENYANDIAN TEXTS CHAT VIA INTERNET DENGAN ALGORITMA VIGENERE CIPHER BERBASIS ANDROID Aulia, Rachmat; Sembiring, Arnes; Zakir, Ahmad; Siregar, Budi Arisa Utomo
Jurnal Sistem Informasi Kaputama (JSIK) Vol. 3 No. 2 (2019): Volume 3, Nomor 2, Juli 2019
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jsik.v3i2.772

Abstract

One feature of a smartphone device that is still often used is the exchange of messages such as text chat. This exchange of information generally knows no distance, can be far or close. This requires security of the confidentiality of information sent from the source to the target. One way to get the security of communication is by applying cryptographic techniques so that the message sent to the target can be secured. The cryptographic technique used in this study is Vigenere Cipher. The Vigenere Cipher algorithm is used to encode text conversations so that messages sent via the internet can be maintained as well as with confidentiality. The purpose of this study is to build a text conversation cryptographic application that can run on Android-based smartphone devices. The technique can encrypt text conversations into secret messages which then the results (messages) are sent using a message sending application such as SMS (Short Message Service), Whatsapp and Line. Then when the message arrives at the destination, the description is done automatically so that the original message can be read.
Penerapan Smart Farming Sebagai Upaya Modernisasi Pertanian Cabai Rahman, Sayuti; Indrawati, Asmah; Sembiring, Arnes; Hartono, Hartono; Zuhanda, Muhammad Khahfi; Ongko, Erianto
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol 6 No 02 (2024): EDISI SEPTEMBER 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v6i02.1050

Abstract

Cabai merupakan salah satu komoditas hortikultura yang memiliki nilai ekonomi tinggi, namun produktivitasnya sering terganggu oleh berbagai penyakit daun yang disebabkan oleh hama, seperti bercak daun, layu fusarium, embun tepung, dan virus kuning. Penyakit-penyakit ini tidak hanya memengaruhi kualitas hasil panen, tetapi juga menyebabkan kerugian ekonomi yang signifikan bagi petani. Untuk mengatasi permasalahan ini, dilakukan pengabdian kepada masyarakat dengan mengimplementasikan teknologi Convolutional Neural Network (CNN) untuk klasifikasi penyakit daun cabai secara cepat dan akurat. Metode yang digunakan melibatkan observasi lapangan untuk mengidentifikasi permasalahan yang dihadapi petani di Desa Lubuk Cuik, Batu Bara, Sumatera Utara. Data berupa gambar daun cabai yang terinfeksi dikumpulkan dan digunakan untuk melatih model CNN. Model yang dikembangkan, efficientChiliNet, mampu mengklasifikasikan penyakit daun cabai dengan akurasi pelatihan 99,8% dan akurasi validasi 96,5%. Aplikasi berbasis web dan desktop kemudian dibuat untuk mempermudah petani dalam mendiagnosis penyakit daun cabai secara mandiri. Aplikasi ini juga disosialisasikan kepada petani melalui pelatihan untuk memastikan implementasi teknologi yang optimal. Hasil pengabdian ini menunjukkan bahwa teknologi berbasis CNN mampu memberikan solusi efektif dalam mengidentifikasi penyakit daun cabai dan membantu petani meningkatkan produktivitas pertanian. Rekomendasi selanjutnya adalah pengembangan fitur tambahan dalam aplikasi untuk memberikan panduan penanganan hama dan integrasi teknologi Internet of Things (IoT) untuk pemantauan lingkungan secara real-time. Dengan pendekatan ini, diharapkan terciptanya modernisasi pertanian berbasis smart farming yang berkelanjutan.
DATA MINING METODE K-NEAREST NEIGHBOUR UNTUK REGRESI DATA PENJUALAN KAIN TEKSTILE Ilham, Fauzi; Sembiring, Arnes; Siregar, Rosyidah
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 6 (2024): JATI Vol. 8 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i6.11974

Abstract

Dalam perindustrian kain tekstil di era serba teknologi pada perkembangan zaman ini sangatlah berperan penting dalam menentukan kesuksesan bisnis dalam menganalisis penjualan kain tekstile untuk memahami strategi pemasaran yang efektif. Maka diperlukan penggunaan pengolahan data (data mining) dengan aplikasi orange untuk me-regresi data penjualan dominan antara kain lapis 1 dan kain lapis 2 dalam melakukan perhitungan prediksi dari sebuah metode K-Nearest Neighbour yang dapat menghasilkan prediksi penjualan dari data yang terkumpul selama 90 hari yang dimana data tersebut terbagi dari data latih dan data uji yang memiliki tingkat akurasi sebesar 0,611 atau 61% sebagai tingkat persentase yang berarti metode ini berada diatas 0 sampai 0,568 sehingga memiliki tingkat akurasi yang baik untuk dipergunakan. Penelitian ini dilakukan untuk prediksi penjualan selama 30 hari kedepan dengan data kain penjualan offline untuk kain lapis 1 sebanyak 90 data dan kain lapis 2 sebanyak 90 data, juga secara online untuk kain lapis 1 sebanyak 90 data dan kain lapis 2 sebanyak 90 data, sehingga hasil dari sebuah prediksi pada tampilan bar plot bahwa tingkat penjualan kain lapis 1 lebih tinggi dibanding penjualan kain lapis 2 setelah dilakukan perhitungan. Hasil ini membuat perusahaan dapat mengetahui bagaimana strategi penjualan dan penyediaan barang untuk selanjutnya
IDENTIFICATION OF BANANA FRUIT USING BACKPROPAGATION METHOD Widodo, Dian; Fauzi, Achmad; Sembiring, Arnes
Journal of Mathematics and Technology (MATECH) Vol. 2 No. 2 (2023): Journal MATECH (November 2023)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v2i2.155

Abstract

Identifikasi jenis buah pisang dan penilaian tingkat kematangannya merupakan proses yang penting dalam industri pertanian dan distribusi. Dalam upaya untuk mengotomatisasi proses ini, penulis menyarankan pendekatan pemaparan buah pisang dan tingkat kematangannya menggunakan jaringan saraf tiruan Backpropagation . Melalui proses pengolahan citra digital, citra atau gambar dari buah pisang akan dilakukan ekstraksi ciri-ciri seperti RGB ( red green blue ), metrik dan eksentrisitas(ciri bentuk). Hasil proses training data citra sebanyak 55 data citra yang diinputkan, diperoleh proses training data jenis pisang dengan 11 iterasi dari inputan maksimum epoch 10000, target error atau performance 0.00642 dengan nilai rata-rata sebesar 80%. Selanjutnya diperoleh proses data pelatihan tingkat kematangan pisang dengan 4 iterasi dari input maksimum epoch 10000, target error atau performance 0.00606 dengan nilai akurasi sebesar 90%. Dari proses uji citra yang telah dilakukan bahwa sistem dapat mengidentifikasi jenis buah pisang beserta tingkat kematangannya berdasarkan inputan ekstraksi fitur dari citra buah pisang. Penelitian ini juga bertujuan untuk menguji dan mengetahui tingkat akurasi penerapan metode Backpropagationdalam mengidentifikasi jenis buah pisang dan tingkat kematangannya.
PREDICTION OF STUDENT PASSING SCORE USING BACKPROPAGATION METHOD (CASE STUDY: SMP NEGERI 1 SEI BINGAI LANGKAT) Sembiring, Jams David Pindona; Gultom, Imeldawaty; Sembiring, Arnes
Journal of Mathematics and Technology (MATECH) Vol. 3 No. 1 (2024): Journal MATECH (May 2024)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v3i1.165

Abstract

Education is one of the most important aspects in life in order to form quality human resources and be able to follow the flow of the increasingly advanced era. Students ' passing scores can be used as useful data to see the development of children who will continue their education at the next level, with the results of high passing scores, can create confidence in these students to continue the level of education they want. But not all students get the passing score in accordance with what they want, due to several factors, for example lack of discipline and not too focused on pursuing grades and some are busy working so that school is forgotten. From these conditions, the SMP Negeri 1 Sei Bingai Langkat need to create a system that can predict the passing score of students who will come. The results of these predictions can be used to recommend a decent and good school for students to enter with a high enough passing score. The process in predicting the passing score of students can be done with a computerized system, one of the processes that can be done is the application of Artificial Neural Networks (Ann) with the use of the method Backpropagation process. With the construction of the system is expected to facilitate and assist SMP Negeri 1 Sei Bingai Langkat in knowing the passing score of their students, so that it can be used as a basis in recommending a school that they deserve to enter as the next level of Education. From the research conducted, the results of the number of output layer errors is still large and has not met the target error of 0.001, namely the value of Mathematics for school exams (US) Ade Christy in Junior High School (SMP) Negeri 1 Sei Bingai Langkat are as follows: maximum value (a) : 99 minimum value (b) : 70.
Saklar Lampu Otomatis dengan Kendali Kendali Android Berbasis Mikrokontroller Irawan, Amri; Hasibuan, Ade; Sembiring, Arnes
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol 3 No 2 (2021): Edisi Oktober
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jikstra.v3i2.396

Abstract

Like in a room or classroom having a light switch for manual control, sometimes it's lazy to move or forget to turn it on and off, resulting in a waste of electricity. By utilizing technology and developing humans can create a technology that makes activities more efficient. This tool is designed to turn the lights on and off automatically and also control manually withcontrol smartphone, for example automatic control, at 06.30 Arduino will give commands to the servo motor to turn off the lights and at 18.00 the Arduino will turn on the lights, thencontrol manual is Android controls. Previously requiring an application on a smartphone , first designed the application using the MIT APP INVENTOR then transferred to Android.
Analisa Perbandingan Metode Arithmetic Mean Filtering dan Metode Konvolusi Pada Citra Bernoise Algama, Bella; Sembiring, Arnes; Hasibuan, Ade Zulkarnain
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol 5 No 2 (2023): Edisi Oktober
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jikstra.v5i2.833

Abstract

Image capture often experiences problems which can be caused by errors in the camera lens or dirt in the image. This interference is called noise or noise. Noise makes an image unclear and damages the quality of an image. There are many types of noise in images, one of which is salt and pepper noise. This noise will give black and white spots on the image like a sprinkling of salt caused by bit errors when sending data, or damage to the storage area so that filtering is needed on the image to improve the image quality to be better than the original image. The filtering technique used for noisy images is arithmetic mean filter and convolution. Arithmetic mean filtering improves image quality by replacing the pixel value with the average value of its neighboring pixels, while the image convolution technique gives a new value to each pixel by performing several calculation functions from that pixel to the pixels around it. To measure the filtered noise which has decreased, the MSE and PSNR parameters are used. The results obtained through MSE and PSNR which are better used in reducing noise are the arithmetic mean filter. The arithmetic mean filter makes images with salt and pepper noise experience a decrease as seen from the resulting PSNR value which is higher, namely 52% compared to the convolution PSNR value of 47%.
Analisis Performa Convolution Neural Network untuk Klasifikasi Hewan Berdasarkan Perbedaan Ukuran Kernels Pane, Ilham Maratua; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): Oktober
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.849

Abstract

This study aims to analyze the impact of kernel size variation in Convolutional Neural Network (CNN) architectures on the performance of animal image classification. The kernel sizes evaluated include 3x3, 5x5, 7x7, and 9x9. Performance was assessed using accuracy metrics and confusion matrix analysis to determine the effectiveness of each model. The results indicate that the 5x5 kernel achieved the highest accuracy and the most balanced classification distribution, while the 9x9 kernel resulted in a significant decline in performance. Excessively large kernels led to the model’s inability to capture local features, causing a high rate of misclassification. In contrast, moderately sized kernels maintained a balance between capturing global context and preserving local detail. These findings highlight the importance of selecting an appropriate kernel size in CNN architecture design to achieve optimal classification results.
Klasifikasi Penyakit Tanaman Cabai Menggunakan Googlenet Pada Citra Daun Harahap, Jaffar Siddik; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.832

Abstract

Red chili pepper (Capsicum annuum L.) is a horticultural commodity that has high economic value, but its production is often hampered by plant disease attacks. To automatically detect diseases in chili leaves, this study uses a deep learning approach with GoogLeNet architecture and transfer learning techniques. This study aims to classify five types of chili leaf diseases, namely Healthy, Leaf Curl, Leaf Spot, Whitefly, and Yellowish, using a model initialized with pretrained weights from ImageNet. Three types of optimizers (Adam, RMSprop, and SGD) were tested to evaluate their effect on classification accuracy. The results showed that Adam performed best with a validation accuracy of 98.80%, followed by RMSprop (98.40%) and SGD (94.00%). The confusion matrix shows that misclassification occurs mainly in the Leaf Curl class, which is often confused with Yellowish, due to visual similarities. Although the classification results were excellent, limitations such as the small size of the dataset (500 images) and the need for further augmentation techniques to address prediction errors remained challenges. This research contributes to the development of an efficient and accurate computer vision-based plant disease classification system.
Analisis Pengaruh Fungsi Aktivasi CNN terhadap Performa Klasifikasi Hewan Ray, Raja Pahlefi; Sembiring, Arnes
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): Oktober
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.847

Abstract

This study aims to analyze the impact of five activation functions ReLU, LeakyReLU, ELU, Sigmoid, and Tanh—on the performance of a Convolutional Neural Network (CNN) model for image classification into three categories: cats, dogs, and wild animals. The evaluation was conducted using validation accuracy metrics, accuracy trends across training epochs, and confusion matrix analysis. The results show that modern activation functions such as LeakyReLU, ELU, and ReLU yield high accuracy and balanced predictions, demonstrating their effectiveness in mitigating vanishing gradient issues and enhancing the model's generalization capability. In contrast, classical functions like Sigmoid and Tanh performed poorly, producing imbalanced predictions and stagnant accuracy Therefore, the choice of activation function plays a critical role in building an optimal CNN model for image classification tasks. This study recommends ReLU-based activation functions, particularly LeakyReLU, as the primary choice for developing multi-class image classification models.
Co-Authors Achmad Fauzi Ahmad Zakir, Ahmad Algama, Bella Asmah Indrawati, Asmah Erianto Ongko Gultom, Imeldawaty Harahap, Jaffar Siddik Hartono Hartono Hasibuan, Ade Hasibuan, Ade Zulkarnain Ilham, Fauzi Irawan, Amri Kristina Annatasia Br Sitepu Marwan Ramli Muhammad Khahfi Zuhanda Muhammad Zen, Muhammad Naufal, Tengku Akhdan Pane, Ilham Maratua Rachmat Aulia, Rachmat Rahman, Sayuti Ray, Raja Pahlefi Rifai, Abas Rizky, Bagaskara Dwi Saifuddin Muhammad Jalil Sembiring, Hermansyah Sembiring, Jams David Pindona Sinaga, Nicolas Novelico Siregar, Budi Arisa Utomo Siregar, Rosyidah Syah, Rahmad B.Y Widodo, Dian