cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Andy Sapta
Contact Email
sapta@royal.ac.id
Phone
-
Journal Mail Official
lppm_stmik@royal.ac.id
Editorial Address
-
Location
Kab. asahan,
Sumatera utara
INDONESIA
JURTEKSI
Published by STMIK Royal Kisaran
ISSN : 24071811     EISSN : 25500201     DOI : -
Core Subject : Science,
Computer Science & IT
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) is a scientific journal which is published by STMIK Royal Kisaran. This journal published twice a year on December and June. This journal contains a collection of research in information technology and computer system.
Arjuna Subject : -
Articles 747 Documents
 71   72   73   74   75 
DEVELOPMENT OF AN AUGMENTED REALITY APPLICATION FOR LEARNING THE VOLUME AND SURFACE AREA OF THREE-DIMENSIONAL SHAPES Andy Sapta; Sondang Purnamasari Pakpahan
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 4 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

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

Abstract

This study focuses on the development of an Augmented Reality (AR)–based learning application designed to assist students in understanding the mathematical concepts of volume and surface area of three-dimensional geometric shapes. The development process adopted the Multimedia Development Life Cycle (MDLC) model, which consists of six systematic stages: concept, design, material collecting, assembly, testing, and distribution. The research concentrated on the development and expert validation stages. Validation results from content and media experts indicate that the application meets pedagogical and technical feasibility standards. The content expert confirmed that the materials align with the national mathematics curriculum and are presented in a clear, contextual, and accurate manner, while the media expert highlighted the user-friendly interface, interactive features, and visual appeal of the application. Theoretically, this AR-based medium bridges the gap between abstract mathematical concepts and concrete visualization by enabling students to interact directly with virtual 3D objects. Practically, the application enhances learning motivation and engagement by providing dynamic, interactive experiences. Overall, this research contributes to the advancement of educational technology by offering a systematic model for developing AR-based learning media that support active and meaningful learning in the digital era.
IMAGE PROCESSING SYSTEM FOR SEMICONDUCTOR CHIP COUNTING AT PT ELEKTRONIK INDONESIA Hasbullah Hasbullah; Agus Indra Gunawan; Setiawardhana
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4314

Abstract

Abstract: Conventional semiconductor chip counting at PT Elektronik Indonesia relies on manual weighing, which is prone to human error and inefficiency. This study proposes a desktop-based counting system using a digital scanner and image processing. The novelty lies in integrating horizontal-vertical projection with probabilistic Hough transform to robustly detect grid lines, form square cells, and enable accurate unit estimation via average intensity analysis, eliminating the need for reference weighing. Experiments on 15 actual chip images yielded an error rate of 0.009519% and up to 73.674%time efficiency gains compared to the manual method. The system reduces operator dependency, minimizes errors, and accelerates counting, providing a practical machine vision solution for semiconductor production. Keywords: chip counting; image processing; probabilistic hough transform; grid line detection; time effeciency. Abstrak: Penghitungan chip semikonduktor konvensional di PT Elektronik Indonesia bergantung pada penimbangan manual, yang rentan terhadap kesalahan manusia dan kurang efisien. Penelitian ini mengusulkan sistem penghitungan berbasis desktop menggunakan scanner digital dan pengolahan citra. Kebaruan terletak pada integrasi proyeksi horizontal-vertikal dengan probabilistic Hough transform untuk mendeteksi garis grid secara kuat, membentuk sel persegi, serta memungkinkan estimasi unit akurat melalui analisis intensitas rata-rata, sehingga menghilangkan kebutuhan penimbangan referensi. Eksperimen pada 15 citra chip aktual menghasilkan tingkat kesalahan 0,009519% dan peningkatan efisiensi waktu hingga 73,674% dibandingkan metode manual. Sistem ini mengurangi ketergantungan operator, meminimalkan kesalahan, dan mempercepat penghitungan, menyediakan solusi machine vision praktis untuk produksi semikonduktor. Kata kunci: penghitungan chip; pengolahan citra; probabilistic Hough transform; deteksi garis grid; efisiensi waktu.
COMPARISON OF RESNET-50 AND DENSENET-121 CNNARCHITECTURES FOR MALARIA IMAGE CLASSIFICATION Betantiyo Prayatna; Kurnia Budi; Fachruddin Fachruddin
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4371

Abstract

Abstract: Malaria remains a major global health problem, particularly in tropical countries such as Indonesia. Accurate early diagnosis is essential for reducing malaria-related morbidity and mortality. Conventional microscopic examination is time-consuming, highly dependent on expert personnel, and prone to human error. This study compares the performance of two Convolutional Neural Network (CNN) architectures, ResNet-50 and DenseNet-121, for malaria image classification. The Cell Images for Malaria dataset provided by the National Institutes of Health (NIH) through Kaggle was used, consisting of 27,558 microscopic blood cell images categorized into Parasitized and Uninfected classes. The dataset was divided into 80% training data and 20% testing data. Image preprocessing included resizing to 224 × 224 pixels, normalization, labeling, and data augmentation using RandomFlip, RandomRotation, RandomZoom, and RandomContrast. Experimental results showed that the ResNet-50 model trained for 100 epochs achieved the highest performance, with an accuracy of 95.54% and precision, recall, and F1-score of 0.96. The confusion matrix indicated 5,272 correctly classified images out of 5,510 testing samples. These findings demonstrate that ResNet-50 outperformed DenseNet-121 and has strong potential for supporting accurate, reliable, and efficient computer-aided malaria diagnosis based on microscopic blood smear images. Keywords: computer-aided diagnosis; convolutional neural network (CNN); densenet-121; early detection; image classification; malaria; microscopic blood smear images; resnet-50; Abstrak : Malaria masih menjadi masalah kesehatan global yang serius, terutama di negara tropis seperti Indonesia. Diagnosis dini yang akurat sangat penting untuk menurunkan angka morbiditas dan mortalitas. Metode konvensional berupa pemeriksaan mikroskopis memiliki keterbatasan karena memerlukan waktu yang relatif lama, bergantung pada tenaga ahli, dan berpotensi menimbulkan kesalahan manusia. Penelitian ini bertujuan membandingkan kinerja arsitektur Convolutional Neural Network (CNN) yaitu ResNet-50 dan DenseNet-121 dalam klasifikasi citra malaria. Dataset yang digunakan berasal dari Cell Images for Malaria yang disediakan oleh National Institutes of Health (NIH) melalui platform Kaggle, terdiri dari 27.558 citra dengan pembagian 80% data latih, 20% data validasi. Tahap praproses meliputi cleaning, resizing citra menjadi 224×224 piksel, normalisasi, labeling, serta data augmentasi menggunakan RandomFlip, RandomRotation, RandomZoom, dan RandomContrast. Hasil pengujian menunjukkan bahwa model ResNet-50 pada epoch 100 memperoleh akurasi sebesar 95,54% dengan nilai precision, recall, dan F1-score masing-masing sebesar 0,96. Confusion matrix menunjukkan jumlah prediksi benar sebanyak 5.272 dari total 5.510 data uji. Hasil ini menunjukkan bahwa arsitektur CNN mampu mengklasifikasikan citra malaria dengan tingkat akurasi yang tinggi dan memiliki kemampuan generalisasi yang baik terhadap data baru. Penelitian ini memberikan kontribusi dalam evaluasi performa arsitektur CNN untuk mendukung pengembangan sistem diagnosis malaria berbasis citra mikroskopis yang lebih cepat dan akurat. Kata kunci: convolutional neural network (CNN); citra mikroskopis hapusan darah; densenet-121; diagnosis berbantuan komputer; deteksi dini; klasifikasi citra; malaria; resnet-50
COMPARATIVE ANALYSIS OF RANDOM FOREST, KNN, AND SVM FOR TODDLER STUNTING CLASSIFICATION Maritza Ayu Shula; Sri Siswanti
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4384

Abstract

Abstract: Stunting is a chronic nutritional condition in toddlers characterized by a Height-for-Age (HFA) measurement below the standard growth threshold, necessitating early detection to prevent long-term consequences. This study aims to classify toddler stunting status by comparing three machine learning methods: Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The dataset comprises 345 toddler records from Puskesmas Indramayu (2025), including weight, height, and nutritional status based on WFA, HFA, and WFH indicators. Preprocessing steps include data cleaning, StandardScaler normalization, One-Hot Encoding for categorical features, and splitting the training and testing data with a ratio of 80:20. The comparison results are that KNN achieved the best performance with an accuracy of 71.01%, a precision of 0.69, a recall of 0.69, and an F1 score of 0.67, while RF and SVM both had an accuracy of 69.57% with F1 scores of 0.67 and 0.68, respectively. Thus, KNN demonstrated superior effectiveness in classifying the stunting status of toddlers compared to RF and SVM on this dataset. Keywords: KNN; Random Forest; SVM; Stunting; toddlers Abstract: Stunting adalah kondisi gizi kronis pada balita yang ditandai dengan pengukuran Tinggi Badan menurut Usia (HFA) di bawah ambang batas pertumbuhan standar, sehingga memerlukan deteksi dini untuk mencegah konsekuensi jangka panjang. Penelitian ini bertujuan untuk mengklasfikasikan status stunting pada balita dengan membandingkan tiga metode pembelajaran mesin: Random Forest (RF), K-Nearest Neighbor (KNN), dan Support Vector Machine (SVM). Kumpulan data terdiri dari 345 catatan balita dari puskesmas indramayu (2025), termaksut brat badan, tinggi badan, dan status gizi berdasarkan indicator WFA, HFA, dan WFH. Langkah-langkah prapemrosesan meliputi pembersian data, normalisasi Stand-ardScaler, One-Hot Encoding untuk fitur kategirikal, serta pembagian data pelatihan dan pengujian dengan rasio 80:20. Hasil perbadingan adalah KNN mencapai kinerja terbaik dengan akurasi 71,01%, presisi 0,69, recall 0,69, dan skor F1 sebesar 0,67, RF dan SVM keduanya memiliki akurasi 69,57% dengan skor F1 masing-masing sebesar 0,67 dan 0,68. Dengan demikian, KNN menunjukkan keefektifan yang lebih unggul dalam mengklasifikasikan status stunting balita dibandingkan dengan RF dan SVM pada da-taset ini. Kata kunci: KNN; random forest; SVM; Stunting; Balita
OPTIMIZING CYBER ATTACK SIMULATION AS A RESPONSE TO ESCALATING SECURITY THREATS USING A MACHINE LEARNING APPROACH Rivaldi Lubis; Apriyanto Halim; Felix Jansen Tanjaya; Tandri
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4411

Abstract

Abstract: The growing intensity of cyber attacks, marked by rapid, large-scale, automated, and adaptive execution, requires analytical methods that represent the diversity of network environments, including variations in target platforms such as IoT, traditional networks, and hybrid infrastructures. This study compares machine learning models for cyber attack classification under heterogeneous environmental conditions and formulates a conceptual optimization framework based on model performance. Four publicly available benchmark datasets were used, namely UNB CIC IoT 2023, UNB CIC IDS-2018, UNSW-NB15, and a Kaggle cyber security attacks dataset, comprising approximately 40,000 to over 3.6 million records and 25 to 80 features across IoT, conventional, and mixed network environments. Random Forest, XGBoost, Multilayer Perceptron, and Transformer were implemented within a unified pipeline involving preprocessing, feature selection, and Bayesian Optimization-based hyperparameter tuning. All models achieved F1-score and Cohen's Kappa above 96%, with XGBoost performing best (97.80%, 97.26%), followed by Random Forest (97.78%, 96.96%) and Transformer (97.44%, 96.82%), while MLP scored lowest (96.74%, 96.00%), a gap below one percentage point. Confusion matrix analysis revealed persistent misclassification in minority and overlapping attack classes, informing a proposed adaptive cyber attack simulation optimization framework. Keywords: cyber attacks; optimization; machine learning; environmental variability. Abstrak: Meningkatnya intensitas serangan siber yang berlangsung cepat, masif, otomatis, dan adaptif menuntut pendekatan analitis yang merepresentasikan keragaman lingkungan jaringan, termasuk perbedaan karakteristik platform sasaran seperti Internet of Things (IoT), jaringan konvensional, dan infrastruktur hibrida. Penelitian ini membandingkan model machine learning untuk klasifikasi serangan siber pada kondisi lingkungan heterogen, sekaligus menyusun kerangka optimasi konseptual berdasarkan performa model. Empat dataset benchmark publik digunakan, yaitu UNB CIC IoT 2023, UNB CIC IDS-2018, UNSW-NB15, serta dataset Kaggle cyber security attacks, dengan jumlah data berkisar 40.000 hingga lebih dari 3,6 juta rekaman dan 25 sampai 80 fitur, mewakili lingkungan IoT, konvensional, dan campuran. Random Forest, XGBoost, Multilayer Perceptron, dan Transformer diimplementasikan melalui pipeline terpadu mencakup pra-pemrosesan, seleksi fitur, dan optimasi hyperparameter berbasis Bayesian Optimization. Seluruh model mencapai F1-score dan Cohen's Kappa di atas 96%, dengan XGBoost menunjukkan performa terbaik (97,80%, 97,26%), diikuti Random Forest (97,78%, 96,96%) dan Transformer (97,44%, 96,82%), sementara MLP mencatat skor terendah (96,74%, 96,00%), dengan selisih kurang dari satu poin persentase. Analisis confusion matrix mengungkap misklasifikasi yang konsisten pada kelas minoritas dan serangan dengan karakteristik serupa, yang menjadi dasar kerangka optimasi simulasi serangan siber adaptif yang diusulkan. Kata kunci: serangan siber; optimasi; machine learning; variabilitas lingkungan
OPTIMIZING CUSTOMER RELATIONSHIPS THROUGH CUSTOMER RELATIONSHIP MANAGEMENTAT HANDMADE WILLY Ria Utari; Riki Andri Yusda; Amalia Amalia
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4446

Abstract

Abstract: The development of globalization and digitalization requires businesses to not only focus on product quality, but also on the ability to build and maintain long-term relationships with customers. Customer loyalty has become a strategic asset that influences business sustainability and competitiveness. Handmade Willy, a creative business engaged in the production and sale of handicrafts, faces various problems in customer management, such as difficulties in identifying customer preferences, limitations in ongoing communication, suboptimal customer segmentation, and the absence of a structured system for monitoring customer satisfaction and feedback. These problems have an impact on the ineffectiveness of marketing strategies and the potential decline in customer loyalty. This study aims to optimize customer relationships at Handmade Willy through the application of the Customer Relationship Management (CRM) concept. The research method used is descriptive analysis with a qualitative approach through data collection from observation, interviews, and literature studies. The blackbox testing results show that the system runs smoothly without any obstacles. The implementation of CRM helps Handmade Willy understand customer characteristics and preferences, perform more accurate segmentation, improve communication effectiveness, and systematically monitor customer satisfaction. Keyword: customer loyalty; customer relationship management; handmade willy. Abstrak: Perkembangan era globalisasi dan digitalisasi menuntut pelaku usaha untuk tidak hanya berfokus pada kualitas produk, tetapi juga pada kemampuan membangun dan mempertahankan hubungan jangka panjang dengan pelanggan. Loyalitas pelanggan menjadi aset strategis yang berpengaruh terhadap keberlanjutan dan daya saing bisnis. Handmade Willy sebagai usaha kreatif yang bergerak di bidang produksi dan penjualan kerajinan tangan menghadapi berbagai permasalahan dalam pengelolaan pelanggan seperti kesulitan dalam mengidentifikasi preferensi pelanggan, keterbatasan komunikasi berkelanjutan, belum optimalnya segmentasi pelanggan serta belum adanya sistem yang terstruktur untuk memantau kepuasan dan umpan balik pelanggan. Permasalahan tersebut berdampak pada kurang efektifnya strategi pemasaran dan potensi penurunan loyalitas pelanggan. Penelitian ini bertujuan untuk mengoptimalkan hubungan pelanggan pada Handmade Willy melalui penerapan konsep Customer Relationship Management (CRM). Metode penelitian yang digunakan adalah analisis deskriptif dengan pendekatan kualitatif melalui pengumpulan data observasi, wawancara dan studi literatur. Hasil pengujian blackbox menunjukkan sistem yang dibuat berjalan dengan lancar tanpa ada kendala. Dengan penerapan CRM mampu membantu Handmade Willy dalam memahami karakteristik dan preferensi pelanggan, melakukan segmentasi yang lebih tepat, meningkatkan efektivitas komunikasi serta memantau kepuasan pelanggan secara sistematis. Kata kunci: customer relationship management; kerajinan tangan willy; loyalitas pelanggan.
A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS AND USER EXPERIENCE FOR ACADEMIC PERFORMANCE PREDICTION Virdyra Tasril; Santi Prayudani; J. Prayoga; Rahayu Mayang Sari
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4534

Abstract

This study aimed to compare the performance of machine learning algorithms and user experience in predicting students’ academic achievement. The research is motivated by the need for prediction systems that are not only highly accurate but also easily interpretable by users. The proposed methodology involved the implementation of two algorithms, namely Decision Tree and Random Forest, using an academic dataset that included grade point average, attendance, and assessment scores. Model performance was evaluated using accuracy, precision, recall, and F1-score, while user experience was assessed through the System Usability Scale (SUS) based on a simple user interface. The findings revealed that Random Forest achieved higher predictive accuracy, whereas Decision Tree provided better interpretability and ease of understanding for users. These results indicated a trade-off between model performance and user experience, suggesting that algorithm selection should consider both aspects in order to develop an effective and user-friendly academic prediction system
PERFORMANCE EVALUATION OF AUTOMATED MEETING SUMMARIZATION BASED ON OPEN AI WHISPER AND INDOT5 FINE-TUNING I Gusti Lanang Oka Wiyana; Putu Indah Ciptayani; Ida Bagus Adisimakrisna Peling
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4581

Abstract

Abstract: Manual meeting documentation risks losing important information due to cognitive fatigue. Although automated summarization models have evolved, integrated end-to-end systems for Indonesian spoken language remain highly limited. This study aims to design and evaluate an end-to-end automated meeting summarization architecture that directly integrates Automatic Speech Recognition (ASR) via OpenAI Whisper for transcription and the IndoT5 language model for abstractive summarization. IndoT5 was fine-tuned using a dataset of 486 Indonesian spoken language transcript pairs. Testing was conducted on a CPU infrastructure using MP4, MP3, and WAV formats. Results show the optimal fine-tuning configuration significantly improved accuracy, achieving ROUGE-1 (0.4167), ROUGE-2 (0.1973), and ROUGE-L (0.2701) scores. Computationally, the system achieved a Real-Time Factor below 1, processing data faster than the actual recording duration. Conclusively, integrating Whisper and IndoT5 shows potential in producing coherent meeting summaries with lightweight computational overhead, making it viable for local infrastructure implementation to ensure data privacy. Keywords: abstractive summarization; ASR; end-to-end pipeline; IndoT5; real-time factor Abstrak: Dokumentasi rapat manual rentan menghilangkan informasi penting akibat keterbatasan kognitif. Meskipun model peringkas otomatis telah berkembang, implementasi sistem terintegrasi (end-to-end) khusus percakapan lisan berbahasa Indonesia masih sangat terbatas. Penelitian ini bertujuan merancang dan mengevaluasi arsitektur peringkas rapat otomatis end-to-end yang mengintegrasikan langsung Automatic Speech Recognition (ASR) melalui OpenAI Whisper untuk transkripsi dan model bahasa IndoT5 untuk peringkasan abstraktif. Adaptasi domain dilakukan melalui fine-tuning IndoT5 menggunakan 486 pasang dataset transkrip lisan berbahasa Indonesia. Pengujian pada infrastruktur CPU menggunakan format MP4, MP3, dan WAV. Hasil pengujian menunjukkan konfigurasi fine-tuning optimal berhasil meningkatkan akurasi, dengan skor ROUGE-1 (0,4167), ROUGE-2 (0,1973), dan ROUGE-L (0,2701). Sistem mendemonstrasikan efisiensi komputasi dengan nilai Real-Time Factor di bawah 1, mengindikasikan waktu pemrosesan lebih cepat dari durasi rekaman asli. Kesimpulannya, integrasi Whisper dan IndoT5 menunjukkan potensi dalam menghasilkan ringkasan yang koheren dengan beban komputasi ringan, sehingga layak diimplementasikan pada infrastruktur lokal organisasi untuk menjaga privasi data. Kata kunci: ASR; end-to-end pipeline; IndoT5; peringkasan abstraktif; real-time factor
DEVELOPMENT OF A DIGITAL E-CRM AS A SOLUTION FOR CUSTOMER RELATIONSHIP MANAGEMENT AND TRANSACTION ACTIVITIES AT TOKO ZUMA Nurul Azzuma; Afdhal Syafnur; Elly Rahayu
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4598

Abstract

Abstract: Toko Zuma faces challenges in optimizing customer relationship management and transaction recording due to conventional manual systems, which hinder real-time loyalty monitoring and sales analysis. This research aims to design a digital Electronic Customer Relationship Management (E-CRM) system as an integrative solution for systematic and centralized management. The design method employs Unified Modeling Language (UML) for requirements analysis, user interface design, and Black-box Testing for validation. Results demonstrate a 100% success rate across all primary modules. The platform features Point Reward, automated transaction management, and live chat for direct interaction. Implementation enables personalized promotional strategies based on accurate data to increase customer retention. In conclusion, the Digital E-CRM system effectively automates business processes, serving as a strategic instrument for transparent and measurable customer relationship management. Keywords: digital e-crm; relationship management; toko zuma; transaction activities. Abstrak: Toko Zuma menghadapi kendala dalam optimalisasi manajemen hubungan pelanggan dan pencatatan transaksi karena masih menggunakan sistem manual konvensional. Penelitian ini bertujuan merancang sistem Electronic Customer Relationship Management (E-CRM) digital sebagai solusi integratif untuk pengelolaan basis data dan transaksi secara terpusat. Metode perancangan meliputi analisis kebutuhan menggunakan Unified Modeling Language (UML), desain antarmuka, dan validasi fungsional melalui Black-box Testing. Hasil pengujian menunjukkan tingkat keberhasilan 100% pada seluruh modul utama. Platform ini dilengkapi fitur Point Reward, manajemen transaksi otomatis, dan media interaksi live chat. Implementasi sistem ini memungkinkan strategi promosi personal berdasarkan data akurat untuk meningkatkan retensi pelanggan. Simpulannya, sistem E-CRM digital berhasil mengotomatisasi proses bisnis dan menjadi instrumen strategis dalam manajemen hubungan pelanggan yang transparan dan terukur. Kata kunci: aktivitas transaksi; digital e-crm; pengelolaan relasi; toko zuma.
INTELLIGENT DIGITAL FORENSICS FILE MANIPULATION DETECTION USING METADATA ANALYSIS AND RANDOM FOREST: ANALISIS METADATA DAN RANDOM FOREST Erwin Gabe Panggabean; Yuda Perwira; Dedi Candro Parulian Sinaga; Nur Lidia Lubis; Muhammad Suheru
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4635

Abstract

Abstract: The advancement of digital technology has made it easier to create, process, and distribute files—using 317 files from the dataset https://www.kaggle.com/datasets/axon data/selfie-and-official-id-photo-dataset-18k images?select=metadata_image.csv has also introduced new challenges, such as the increasing practice of digital file manipulation that is difficult to detect visually. Therefore, an intelligent digital forensics system that can automatically and accurately detect file authenticity is required. This study aims to develop an intelligent digital forensics system for detecting file manipulation by leveraging metadata analysis and the Random Forest classification method. The methods used include extracting metadata from digital files—such as time information, device details, and processing history—followed by analysis to identify patterns of inconsistency that indicate manipulation. This data is then used as features in the classification process using the Random Forest algorithm to distinguish between original and manipulated files. The results of this study are expected to show that the use of metadata analysis combined with the Random Forest algorithm can improve accuracy in detecting digital file manipulation compared to conventional methods. The resulting system is expected to provide an effective, efficient, and integrated solution to support digital forensic investigations, Based on the test results, the system demonstrated good performance with an accuracy rate of 94%. Keywords: Digital Forensics;File Manipulation;Metadata Analysis;Random Forest;Classification;Machine Learning Abstrak:Perkembangan teknologi digital telah meningkatkan kemudahan dalam pembuatan, pengolahan,dan distribusi file sebanyak 317 file, sumber datasets https:// www.kaggle.com/datasets/axondata/selfie-and-official-id-photo-dataset-18k-images?select =metadata_image.csv, namun juga menimbulkan tantangan baru berupa meningkatnya praktik manipulasi file digital yang sulit dideteksi secara kasat mata. Oleh karena itu, diperlukan suatu sistem forensik digital yang cerdas dan mampu mendeteksi keaslian file secara otomatis dan akurat. Penelitian ini bertujuan untuk mengembangkan sistem forensik digital cerdas untuk deteksi manipulasi file dengan memanfaatkan analisis metadata dan metode klasifikasi Random Forest. Metode yang digunakan meliputi proses ekstraksi metadata dari file digital, seperti informasi waktu, perangkat, dan riwayat pengolahan, kemudian dilakukan analisis untuk menemukan pola ketidaksesuaian yang mengindikasikan adanya manipulasi. Selanjutnya, data tersebut digunakan sebagai fitur dalam proses klasifikasi menggunakan algoritma Random Forest untuk membedakan antara file asli dan file yang telah dimanipulasi. Hasil dari penelitian ini diharapkan menunjukkan bahwa penggunaan analisis metadata yang dikombinasikan dengan algoritma Random Forest mampu meningkatkan akurasi dalam mendeteksi manipulasi file digital dibandingkan metode konvensional. Sistem yang dihasilkan dapat memberikan solusi yang efektif, efisien, dan terintegrasi dalam mendukung proses investigasi forensik digital, Berdasarkan hasil pengujian, sistem menunjukkan performa yang baik dengan tingkat akurasi sebesar 94%. Kata Kunci: Forensik Digital, Manipulasi File, Metadata, Random Forest, Klasifikasi, Machine Learning.

Page 74 of 75 | Total Record : 747

 71   72   73   74   75 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol. 12 No. 3 (2026): Juni 2026 Vol. 12 No. 2 (2026): Maret 2026 Vol. 12 No. 1 (2025): Desember 2025 Vol. 11 No. 4 (2025): September 2025 Vol. 11 No. 3 (2025): Juni 2025 Vol 11, No 3 (2025): Juni 2025 Vol. 11 No. 2 (2025): Maret 2025 Vol 11, No 2 (2025): Maret 2025 Vol 11, No 1 (2024): Desember 2024 Vol. 11 No. 1 (2024): Desember 2024 Vol 10, No 4 (2024): September 2024 Vol. 10 No. 4 (2024): September 2024 Vol. 10 No. 3 (2024): Juni 2024 Vol 10, No 3 (2024): Juni 2024 Vol. 10 No. 2 (2024): Maret 2024 Vol 10, No 2 (2024): Maret 2024 Vol 10, No 1 (2023): Desember 2023 Vol. 10 No. 1 (2023): Desember 2023 Vol 9, No 4 (2023): September 2023 Vol. 9 No. 4 (2023): September 2023 Vol 9, No 3 (2023): Juni 2023 Vol 9, No 2 (2023): Maret 2023 Vol 9, No 1 (2022): Desember 2022 Vol 8, No 3 (2022): Agustus 2022 Vol 8, No 2 (2022): April 2022 Vol 8, No 1 (2021): Desember 2021 Vol 7, No 3 (2021): Agustus 2021 Vol 7, No 2 (2021): April 2021 Vol 7, No 1 (2020): Desember 2020 Vol 6, No 3 (2020): August 2020 Vol 6, No 2 (2020): April 2020 Vol 6, No 1 (2019): December 2019 Vol 5, No 2 (2019): Juni 2019 Vol 5, No 1 (2018): Desember 2018 Vol 4, No 2 (2018): Juni 2018 Vol 4, No 1 (2017): Desember 2017 More Issue