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(LPPM)LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern Jl. Diponegoro No.69, Dusun I, Wirogunan, Kec. Kartasura, Kabupaten Sukoharjo, Jawa Tengah 57166
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Teknik: Jurnal Ilmu Teknik dan Informatika
Published by Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern
ISSN : 28088751     EISSN : 27982513     DOI : 10.51903
Core Subject : Science,
Computer Science & IT
Jurnal Ilmu Teknik dan Informatika (TEKNIK) menerbitkan satu-satunya makalah yang secara ketat mengikuti pedoman dan template TEKNIK untuk persiapan naskah. Semua manuskrip yang dikirimkan akan melalui proses peer review double-blind. Makalah tersebut dibaca oleh anggota redaksi (sesuai bidang spesialisasi) dan akan disaring oleh Redaktur Pelaksana untuk memenuhi kriteria yang diperlukan untuk publikasi TEKNIK. Naskah akan dikirim ke dua reviewer berdasarkan pengalaman historis mereka dalam mereview naskah atau berdasarkan bidang spesialisasi mereka. TEKNIK telah meninjau formulir untuk menjaga item yang sama ditinjau oleh dua pengulas. Kemudian dewan redaksi membuat keputusan atas komentar atau saran pengulas. Reviewer memberikan penilaian atas orisinalitas, kejelasan penyajian, kontribusi pada bidang/ilmu pengetahuan. Jurnal ini menerbitkan artikel penelitian (research article), artikel telaah/studi literatur (review article/literature review), laporan kasus (case report) dan artikel konsep atau kebijakan (concept/policy article), di semua bidang : Network Computer and Security Computer Architecture Design Data Mining Human Computer Interaction Sistem pakar (Expert System) Jaringan syaraf tiruan (Artificial Neural Network) Algoritma genetic. Penalaran komputer berbasis kasus (Case Based Reasoning) Agen Cerdas (Intelligent Software Agents) Geographical Information System
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 109 Documents
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Analisis Komparatif XGBoost dan Temporal Fusion Transformer (TFT) pada Time Series Forecasting Harga Solana Herdiyanto, Qatrunnada Athirah; Juhraini Helfiana Lexa; Chan, M. Zikry Sahendra
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1138

Abstract

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.
Perbandingan Algoritma Divide and Conquer dan Searching pada Pengolahan Data Nilai Mahasiswa Berbasis Web Mevia, Nazwa Aidilia Octa; Marbun, Yohana Kartika; Putri, Melika Debiyana; Sitompul, Yunanda Rizki
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v1i1.1145

Abstract

The rapid digital transformation in educational institutions demands an efficient student grade data processing system capable of handling workloads responsively. This study aims to analyze and compare the efficiency of sorting algorithms (Merge Sort and Quick Sort) and searching algorithms (Linear Search and Binary Search) on a web-based platform. The research method employed is laboratory experimental, testing algorithm performance across various data volume stratifications, ranging from 50 to 1000 entities, using the V8 JavaScript engine. Research findings indicate that Quick Sort possesses superior speed compared to Merge Sort due to its efficient in-place sorting architecture, which minimizes memory overhead and Garbage Collection activity. Furthermore, a performance anomaly was discovered where the Just-In-Time (JIT) Compiler mechanism optimizes execution on large data volumes through a warm-up phase. In the searching aspect, Binary Search demonstrates superior O(log n) logarithmic stability compared to Linear Search, which risks causing interface freezing on massive data. The implication of this study is the critical importance of implementing data pre-sorting protocols to exploit logarithmic search speeds to ensure academic information system scalability. The integration of appropriate algorithms proves to be a crucial foundation in maintaining web application responsiveness amidst the ever-increasing escalation of educational data.
Perancangan Sistem Penerjemah Bahasa Isyarat bagi Tunarungu dan Tunawicara Berbasis Pengolahan Citra Digital dan Text-to-Speech Trianto, Nafil Rizq; Wijaya, Alfarizi; Pardede, Arion; Pandiangan, Daniel; Syahputra, Hermawan
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1156

Abstract

Communication is an essential human right, yet a significant communication gap persists between individuals with sensory disabilities, specifically the deaf and speech-impaired, and the general public. While many technological solutions have been proposed to translate sign language, existing models primarily rely on heavy deep learning architectures such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN/LSTM). These models often demand high computational power, leading to latency and limiting real-time application on standard devices. This study proposes a lightweight, fast, and highly responsive sign language translation system specifically designed to recognize static alphabets (A-Z) and single-character air writing. The system utilizes MediaPipe for hand tracking, where feature extraction is intelligently processed by calculating the relative spatial coordinates of fingertips to the wrist, reducing dependency on raw camera coordinates. Classification is performed using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, prioritizing computational efficiency without sacrificing accuracy. To enhance user experience, the system introduces three key novelties: smart relative feature extraction, an anti-duplication hold system with a 1-second timer to prevent input spamming, and a non-blocking multithreaded audio execution (Daemon Thread) utilizing Google Text-to-Speech (gTTS), ensuring the webcam feed remains fluid during audio playback. Additionally, an alternative air-writing mode is integrated, utilizing geometric heuristics and PyTesseract OCR to read single drawn letters in the air. The results indicate that the proposed system operates swiftly and efficiently, bridging the communication barrier with a hardware-friendly approach.
Perancangan Aplikasi Pelaporan Kerusakan Infrastruktur Publik Berbasis Android di Pulau Lepar Kabupaten Bangka Selatan untuk Mendukung Konsep Smart Island Adinda Nayla; Reza Al Fajar
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1165

Abstract

This study aims to design and develop an Android-based public infrastructure damage reporting application on Lepat Island, South Bangka Regency, as an effort to support the Smart Island concept. The background of this study is the continued use of a manual reporting system that causes delays in handling and a lack of transparency. The method used is Agile which includes the stages of system design, implementation, testing and periodic evaluation. Data collection techniques are carried out through observation, interviews, and literature studies. The results of the study are an Android-based application that allows the public to report infrastructure damage with features to upload photos, descriptions, and GPS-based locations. In addition, the application provides report history and status monitoring features to increase transparency. The implementation results show that the use of mobile technology can improve service efficiency, speed up the reporting process, and increase community participation. This application is expected to support the implementation of Smart Governance and Smart Island in the region.
Pengembangan Prototipe Detektor Kebakaran Cerdas dengan Sensor Suhu, Kelembapan, dan Api Berbasis IoT (Studi Kasus: Dinas Pendidikan Kabupaten Semarang) Azani Fajri, Laksamana Rajendra Haidar; Mandaya, Yusuf Wisnu; Adhitya Purboyo; Syafi'i, Imam; Yunus, Ryan
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 2 (2025): Oktober : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i2.1224

Abstract

Fire disasters can occur at any time in residential areas or schools, which are often triggered by electrical short circuits, the use of gas stoves, to minor negligence such as cigarette butts. As a preventive effort of Department of Education of Semarang, this research aims to create a prototype of a microcontroller-based early detection and fire suppression system with C programming. This tool uses NodeMCU as a control center that integrates fire sensors and DHT11 sensors to monitor room temperature in real-time. If the system detects any indication of fire or a significant temperature spike, a buzzer will activate as a warning alarm and the fan will work automatically to assist the initial extinguishing process.
Rancang Bangun Sistem Diskusi Hukum Islam Berbasis Multi-Agent AI dengan Metode Retrieval-Augmented Generation (RAG) pada Platform Web Bahtsul Masail Yunus, Ryan; Fajri , Laksamana Rajendra Haidar Azani
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 2 (2025): Oktober : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i2.1225

Abstract

The development of generative artificial intelligence has opened opportunities for strengthening Islamic legal learning through a digital forum that remains connected to authoritative textual references. This study aims to design and build a web-based bahtsul masail discussion system using a multi-agent AI architecture and the Retrieval-Augmented Generation (RAG) method. The proposed system simulates the deliberative roles commonly found in pesantren-based Islamic legal discussion, namely Moderator, Mubahits, Mu'aridh, and Mushahih. The research applies a Research and Development approach with the Waterfall model, covering requirement analysis, interface design, multi-agent workflow design, implementation, functional testing, and evaluation. RAG is implemented by allowing users to upload PDF documents of kitab kuning and assign the documents to particular agent roles. The uploaded texts are then used as contextual grounding so that each agent can formulate arguments, rebuttals, and final decisions based on traceable references rather than unsupported model memory. The application is implemented using HTML, CSS, and JavaScript on the front end, while the AI reasoning process is orchestrated through an API-based large language model. Functional testing shows that the system can complete five sequential stages of bahtsul masail, display role-based responses, manage uploaded references, and present discussion history. The main contribution of this study is a system design that combines pesantren deliberation procedures with AI-based retrieval support to provide an interactive learning medium for Islamic law, while emphasizing that the final authority of legal validation remains with qualified scholars
Implementasi Data Mining Menggunakan Metode RapidMiner Untuk Optimasi Manajemen Akademik Di SMK Secang Niha Syufa’a; Juwari Juwari; Muhammad Ikrar Yamin; Ahmad Soderi; Rinaldo Rinaldo
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1214

Abstract

 Education in vocational high schools (SMKs) requires effective data management to improve students’ academic achievement and discipline. At SMK Islam Secang, students’ academic scores and attendance data have so far functioned merely as administrative archives, making it difficult to identify patterns of student performance. This study aims to classify students based on academic achievement and discipline by applying the K-Means Clustering algorithm using RapidMiner. The data used in this study consist of scores from six subjects and attendance records of 35 students from the Light Vehicle Engineering (TKR) department over two semesters. The data were obtained from original school records, compiled using Microsoft Excel, and processed in RapidMiner. The clustering process employed four clusters for academic achievement and two clusters for discipline, with Euclidean Distance used as the similarity measure. The results show that in the first semester, students were grouped into four academic achievement clusters: high achievement (6 students), moderate achievement (7 students), potentially problematic (14 students), and problematic (8 students). In the second semester, the distribution changed to high achievement (19 students), moderate achievement (14 students), potentially problematic (4 students), and problematic (1 student). Meanwhile, student discipline was divided into two clusters: disciplined (31 students) and undisciplined (4 students). These results demonstrate that K-Means Clustering is effective in mapping student conditions, revealing patterns in academic performance and attendance, and supporting educational evaluation, learning planning, and early detection of students who require academic or disciplinary intervention. Keywords: Data Mining, K-Means Clustering, Academic Achievement, Discipline, RapidMiner, Vocational High School (SMK)
Analisi Sentimen Masyarakat Terhadap Objek Wisata Di Kabupaten Lahat Menggunakan Algoritma Support Vector Machine Nadia Damayanti; Shinta Puspasari; Nazori Suhandi
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1226

Abstract

Nature tourism is one of the sectors that plays an important role in supporting the development of regional tourism, including in Lahat Regency, which has significant waterfall tourism potential. Currently, many visitors share their reviews and experiences through digital platforms such as Google Maps. This review can be used as a source of information to understand the public's evaluation of the quality of tourist attractions. This study aims to examine public perception of tourist attractions in Lahat Regency using the Support Vector Machine (SVM) method. Research data were collected through scraping from Google Maps, totaling 500 reviews from five tourist attractions, namely Curup Maung, Curup Buluh, Senyawe Waterfall, Panjang Waterfall, and Green Canyon. The research stages include data preprocessing, consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming. After that, feature extraction was carried out using the TF-IDF method and the classification process using the SVM algorithm. Based on the research results, the Support Vector Machine (SVM) method is able to perform sentiment classification quite well, although the accuracy level varies for each tourist attraction. Curup Maung and Panjang Waterfall achieved the highest accuracy level of 90%. Nevertheless, most visitor reviews were dominated by negative sentiments. This indicates that there are still several aspects that need to be improved, particularly related to tourist facilities and services. This research is expected to serve as a consideration for tourism managers and local governments in efforts to improve management quality as well as the development of tourism in Lahat Regency.
Analisis Sentimen Komentar Netizen Terhadap 17+8 Tuntutan Rakyat Pada X Menggunakan Naive Bayes Classifier Fransisco Lucky Halawa; Rudi Heriansyah; Indah Permatasari
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1228

Abstract

This study analyzes netizen sentiment concerning the 17+8 public aspirations circulating the digital platform X spanning the period from August 18 through October 31, 2025. 1,837 comments obtained through scraping method. Classification Research stages include data preprocessing, sentiment weighting based on lexicon, and feature extraction using TF-IDF. Data 80% used for learning purposes and the remaining 20% utilized for validation. The findings reveal that the majority of comments, amounting to 81.14%, contained negative sentiment, while the remaining 18.86% were positive. The outcomes demonstrate that community reactions toward the 17+8 People's Demands were dominated by unsupportive views. From a theoretical standpoint this scholarly work offers to enriching knowledge concerning public opinion classification on political issues through a computational approach, while also serving as a reference for future research focused on improving the accuracy of sentiment analysis related to political dynamics and the behavior of state institutions.

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