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All Journal Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Teknisi Jurnal IPTEK Bagi Masyarakat Jurnal Citrakara The Indonesian Journal of Computer Science
Gibran, M Khalil
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mechanical Engineering Physics Public Health Social Sciences

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Peningkatan Motivasi Guru dalam Penelitian Kuantitatif Melalui Pelatihan SPSS di SD Swasta Taman Cahaya Pematangsiantar Lubis, Nur Azizah; Sihombing, Juliana Sion; Andilala, Andilala; Safriana, Safriana; Bagas F, Muhammad; Salim, Salim; Lubis, Muhammad Arif Fadhillah; Gibran, M Khalil
Jurnal IPTEK Bagi Masyarakat Vol 4 No 2 (2024)
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/j-ibm.v4i2.1000

Abstract

Penelitian ini bertujuan meningkatkan motivasi guru dalam melakukan penelitian kuantitatif melalui pelatihan penggunaan perangkat lunak SPSS. Kegiatan pengabdian masyarakat dilaksanakan di SD Swasta Taman Cahaya Pematang Siantar, dengan 21 guru sebagai partisipan. Tahapan kegiatan meliputi observasi awal (wawancara dan survei), penyusunan materi pelatihan, pelaksanaan pelatihan, dan evaluasi. Pelatihan difokuskan pada pendampingan guru dalam merancang dan melakukan penelitian terkait evaluasi media pembelajaran. Hasil evaluasi pelatihan merepresentasikan level keberhasilan yang signifikan pada beberapa aspek, diantaranya pelaksanaan pelatihan (85,71%), manfaat kegiatan (88,10%), penyampaian materi oleh narasumber (91,67%), kesesuaian tema pelatihan (86,90%), dan keseluruhan pelaksanaan kegiatan pelatihan (88,10%). Hasil rata-rata persentase penilaian peserta pelatihan berada pada level 88,10%. Secara keseluruhan, pelatihan SPSS diharapkan dapat berdampak positif dalam meningkatkan kapasitas dan motivasi guru untuk penelitian kuantitatif. Dengan hasil pelatihan, diharapkan guru mampu mengintegrasikan hasil penelitian kuantitatif dalam proses pembelajaran di sekolah untuk mendukung penentuan penggunaan media pembelajaran yang efektif berdasarkan hasil evaluasi.
Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes Azmi, Fadhillah; Gibran, M Khalil; Ridwan, Achmad
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3232

Abstract

In an effort to ensure a safe and high-quality water supply, the assessment of water potability is of paramount importance. An accurate and efficient assessment of water potability can be a challenge due to various influencing factors. Therefore, an innovative and integrated approach is needed to improve the assessment of water potability. In this study, we introduce a new approach to improving the assessment of water potability. This approach aims to overcome the shortcomings of traditional methods by using a hybrid fuzzy-Naïve Bayes approach to obtain a more accurate level of water potability. Fuzzy techniques are used to overcome uncertainty and ambiguity in the initial data. This method describes the probability weights in a fuzzy manner for various parameters. Then, the Naïve Bayes method is used to classify water samples based on the probability generated by the fuzzy system. This hybrid approach makes it possible to consider the relationship between parameters and generate more realistic probability values. This study uses datasets collected from various sources that include water potability parameters. A hybrid fuzzy-Naïve Bayes approach was then applied to this data set to make a more effective and accurate assessment of water potability. The experimental results show that the proposed method obtains an accuracy of 90%, which significantly improves the water potability assessment compared to the conventional method, which results in an accuracy of 84%. By combining fuzzy and Naïve Bayes techniques, we can overcome uncertainty in data and produce more accurate judgments.
Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases Saleh, Amir; Ridwan, Achmad; Gibran, M Khalil
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3379

Abstract

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.
Implementasi Pengolahan Citra Untuk Identifikasi Motor Menggunakan Algoritma Convolutional Neural Network (CNN) Ramadhani, Fredy Kusuma; Hasrul Hsb, Mhd Fikry; Alfaruqy, OK. Mhd Fahri; Nurzanah, Laila; Gibran, M Khalil
CITRAKARA Vol. 7 No. 2 (2025): JULI 2025
Publisher : Universitas Dian Nuswantoro

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

Abstract

Pengolahan citra digital merupakan suatu disiplin ilmu yang berfokus pada transformasi citra menjadi informasi yang dapat diinterpretasikan oleh manusia. CNN, yang terinspirasi oleh arsitektur sistem saraf biologis, dirancang khusus untuk mengolah data dua dimensi dan telah terbukti efektif dalam tugas klasifikasi citra. Dalam studi ini, data diperoleh dari sumber terbuka (Kaggle) dan dikategorikan menjadi dua kelas: sepeda motor dan non-sepeda motor, dengan proporsi 80% untuk pelatihan dan 20% untuk pengujian. Proses pra-pemrosesan mencakup langkah-langkah seperti pengubahan ukuran, normalisasi, dan augmentasi data untuk meningkatkan keragaman dan mencegah fenomena overfitting. Arsitektur CNN dirancang dengan beberapa komponen utama, termasuk input layer, convolutional layer, pooling layer, dan fully connected layer. Hasil evaluasi menunjukkan bahwa model berhasil mengklasifikasikan citra sepeda motor dengan tingkat keyakinan mencapai 99,26% dan mampu mengidentifikasi objek non-sepeda motor dengan akurasi 100%. Temuan ini menegaskan bahwa teknik CNN sangat efektif dalam klasifikasi citra digital, khususnya dalam konteks pengenalan sepeda motor.
USING LEAST SIGNIFICANT BIT TECHNIQUE IN STEGANOGRAPHY TO HIDE INFORMATION haikal, Fikri; Badria, Lailatul; Hendrawan, Aurellia Aknesia; Muharram, Muhammad Raihan; Gibran, M Khalil
JURNAL TEKNISI Vol 5, No 2 (2025): Agustus 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v5i2.3624

Abstract

Abstract: In the digital information era, Steganography is a technique and art of hiding digital information and data behind other digital information, so that the real digital information is not visible. This study discusses the application of the Least Significant Bit (LSB) technique in steganography to hide information in image media. The LSB technique allows the insertion of secret messages into images by utilizing the least significant bits of the image pixels, so that the changes that occur are invisible to the human eye and do not arouse suspicion. This study explores the effectiveness of the LSB technique in maintaining data confidentiality, as well as analyzing image quality after information insertion. The results of the study indicate that the LSB technique can be used effectively to hide information without sacrificing the visual quality of the image, making it an attractive solution in the field of data communication security. In addition, this study also discusses the advantages and disadvantages of the LSB technique, as well as the possibility of developing this technique to improve data security in various applications.Keywords: data security; least significant bit (LSB) technique; steganography.Abstrak: Di era informasi digital, Steganografi merupakan teknik dan seni menyembunyikan informasi dan data digital di balik informasi digital lainnya, sehingga informasi digital yang sebenarnya tidak terlihat. Penelitian ini membahas penerapan teknik Least Significant Bit (LSB) dalam steganografi untuk menyembunyikan informasi di dalam media gambar. Teknik LSB memungkinkan penyisipan pesan rahasia ke dalam gambar dengan memanfaatkan bit paling signifikan dari piksel gambar, sehingga perubahan yang terjadi tidak terlihat oleh mata manusia dan tidak menimbulkan kecurigaan. Penelitian ini mengeksplorasi efektivitas teknik LSB dalam menjaga kerahasiaan data, serta menganalisis kualitas gambar setelah penyisipan informasi. Hasil penelitian menunjukkan bahwa teknik LSB dapat digunakan secara efektif untuk menyembunyikan informasi tanpa mengorbankan kualitas visual gambar, menjadikannya solusi yang menarik di bidang keamanan komunikasi data. Selain itu, penelitian ini juga membahas kelebihan dan kekurangan teknik LSB, serta kemungkinan pengembangan teknik ini untuk meningkatkan keamanan data di berbagai aplikasi.Kata Kunci: keamanan data; teknik least significant bit (LSB); steganografi.
Herbal Plant Image Retrieval Using HSV Color Histogram and Random Forest Algorithm Azmi, Fadhillah; Gibran, M Khalil; Saleh, Amir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26495

Abstract

Herbal plants have significant importance in traditional medicine and are often useful in various natural health products. Visual identification of these plants is usually carried out based on the shape of the leaves and often encounters difficulties in distinguishing species due to similarities in shape and color. Therefore, a system capable of automatically and efficiently recognizing and searching for herbal plant images is needed. This study aims to implement an image search engine for herbal plants based on leaf color similarity. The method used includes color feature extraction using an HSV (Hue, Saturation, Value) histogram with an 8×8×8 bin configuration, resulting in a 512-dimensional feature vector. This histogram feature is then used as input for the Random Forest classification algorithm to group images based on the type of herbal plant. The dataset used consists of 450 herbal leaf images from 9 different classes, obtained through direct image capture using a digital camera. The test results indicates that the developed system is able to classify types of herbal plants with an accuracy of 95.56%. In addition, the computation time and system response during both training and testing processes are relatively fast and efficient. The advantage of this system lies in the simplicity of feature extraction while still being able to provide high classification performance. This system has great potential to be used as an educational tool as well as an initial component in the development of mobile applications for automatic herbal plant identification.
Co-Authors Achmad Ridwan Alfaruqy, OK. Mhd Fahri Amir Saleh Andilala Badria, Lailatul Bagas F, Muhammad Fadhillah Azmi, Fadhillah FIKRI HAIKAL Hasrul Hsb, Mhd Fikry Hendrawan, Aurellia Aknesia Lubis, Muhammad Arif Fadhillah Muharram, Muhammad Raihan Nur Azizah Lubis Nurzanah, Laila Ramadhani, Fredy Kusuma Safriana Safriana Salim Salim SIHOMBING, JULIANA SION