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All Journal Jurnal Teknologi Pendidikan CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Pendidikan dan Konseling JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pengabdian kepada Masyarakat JEBDEKER: Jurnal Ekonomi, Manajemen, Akuntansi, Bisnis Digital, Ekonomi Kreatif, Entrepreneur Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Bureaucracy Journal : Indonesia Journal of Law and Social-Political Governance JURNAL TEKNOLOGI INFORMASI & KOMUNIKASI DALAM PENDIDIKAN Sisfo: Jurnal Ilmiah Sistem Informasi Electron: Jurnal Ilmiah Teknik Elektro Journal of Educational Technology and Learning Creativity Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Journal of Information System and Innovative Technology
Agnes Irene Silitonga
Universitas Negeri Medan
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Perbandingan Scarcity Promotions Pada Pengguna Shopee dan Tokopedia Agnes Irene Silitonga; Prayoga, Wira; Silitonga, Agnes Irene
Jurnal Ekonomi, Manajemen, Akuntansi, Bisnis Digital, Ekonomi Kreatif, Entrepreneur (JEBDEKER) Vol 5 No 1 (2024): Desember 2024
Publisher : Sekolah Tinggi Ilmu Ekonomi Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56456/jebdeker.v5i1.797

Abstract

Penelitian ini bertujuan untuk membandingkan efektivitas dua strategi Scarcity Promotions, yaitu limited time scarcity (LTS) dan limited quantity scarcity (LQS), pada pengguna dua platform e-commerce terkemuka di Indonesia: Shopee dan Tokopedia. Melalui pendekatan kuantitatif, penelitian ini mengumpulkan data dari 270 responden yang merupakan pengguna aktif kedua platform tersebut. Penelitian menunjukkan adanya perbedaan signifikan dalam respons pengguna terhadap Scarcity Promotions. Pengguna Shopee lebih bereaksi positif terhadap limited time scarcity, sedangkan pengguna Tokopedia menunjukkan respons yang lebih moderat terhadap kedua jenis promosi. Analisis statistik T-test menunjukkan nilai signifikan pada variabel LTS dengan nilai t sebesar 4.399 (p < 0.001), dan pada variabel LQS dengan nilai t sebesar 5.141 (p < 0.001), yang mengindikasikan perbedaan nyata dalam persepsi antara pengguna Shopee dan Tokopedia terhadap kelangkaan produk dan waktu. Temuan ini menunjukkan bahwa strategi Scarcity Promotions yang diterapkan oleh Shopee lebih efisien dalam menciptakan urgensi pembelian dibandingkan dengan Tokopedia.
Klasterisasi Penyebaran Base Transceiver Station Menggunakan K-Means Clustering Agnes Irene Silitonga; Silitonga, Agnes Irene; Nasution, Mutiara Akbar; Fitri, Anisa; Rizwinie, Khesya Sabila; Hidayatullah, Amar; Simamora, Yoakim
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 9, No 1 (2025): SEMNAS RISTEK 2025
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v9i1.7947

Abstract

Penelitian ini bertujuan untuk melakukan klaterisasi penyebaran Base Transceiver Station (BTS) 4G dan 5G di Sumatera Utara menggunakan K-Means Clustering. Data yang digunakan diperoleh dari sumber terbuka Kementerian Komunikasi dan Informatika Republik Indonesia. Elbow Method digunakan untuk menentukan jumlah klaster optimal, yaitu tiga klaster. Hasil analisis menunjukkan tiga klaster utama penyebaran BTS di Sumatera Utara: klaster dengan jumlah BTS sangat tinggi (Kota Medan dan Deli Serdang), klaster dengan jumlah BTS tinggi (Asahan, Langkat, Serdang Bedagai, dan Simalungun), dan klaster dengan jumlah BTS rendah (27 kabupaten/kota lainnya). Penelitian ini memberikan informasi berharga bagi pemerintah dan penyedia layanan telekomunikasi untuk mengidentifikasi daerah-daerah yang membutuhkan perhatian khusus dalam pengembangan infrastruktur jaringan internet, sehingga dapat meningkatkan aksesibilitas masyarakat terhadap layanan digital dan mengurangi kesenjangan digital di Sumatera Utara.
Optimasi Penempatan dan Penentuan Kapasitas Distributed Generator Menggunakan Cucko Search Algorithm untuk Mengurangi Rugi Daya Yoakim Simamora; Muhammada Aulia Rahman S; Mega Silfia Dewy; Agnes Irene Silitonga; Lisa Melvi Ginting
ELECTRON Jurnal Ilmiah Teknik Elektro Vol 6 No 2: Jurnal Electron, November 2025
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/electron.v6i2.409

Abstract

Power losses in electrical distribution systems remain a major challenge that significantly impacts energy efficiency and system reliability. One promising approach to address this issue is the optimal placement and sizing of Distributed Generators (DGs) within the distribution network. This study aims to optimize DG placement and capacity using the Cuckoo Search Algorithm (CSA) and to compare its performance with several other algorithms, namely the Black Squirrel Optimization Algorithm (BSOA), Sine Cosine Algorithm (SCA), Teaching Learning Based Optimization - Grey Wolf Optimizer (TLBO-GWO), and GWO. The study was conducted on the IEEE 33-bus test system under two scenarios, with the initial condition of the distribution system exhibiting a power loss of 202.7 kW. In First Case Study, CSA achieved the lowest power loss of 105.31 kW, corresponding to a 48.05% reduction. In contrast, BSOA and TLBO-GWO reduced losses to 116.67 kW (42.44%) and 128.46 kW (36.62%) respectively. In Second Case Study, CSA again demonstrated superior performance with a loss reduction of 56.66%, outperforming SCA (56.33%), BSOA (55.97%), and GWO (55.82%). The optimal DG placement and sizing significantly improved overall system efficiency. The results indicate that CSA possesses strong exploration and convergence capabilities in identifying optimal DG configurations. Its application enables greater reduction in power losses while also enhancing voltage profiles and system stability. These findings suggest that CSA is an effective and competitive method for power distribution optimization involving distributed generation
Security Evaluation of Indonesian LLMs for Digital Business Using STAR Prompt Injection Agnes Irene Silitonga; Irwandi, Hafiz; Silitonga, Agnes Irene; Rudy Chandra; Simamora, Windi Saputri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15662

Abstract

The adoption of Large Language Models (LLMs) in digital business systems in Indonesia is rapidly increasing; however, systematic security evaluation against Indonesian language prompt injection remains limited. This study introduces the Indonesian Prompt Injection Dataset, consisting of 50 attack scenarios constructed using the STAR framework, which combines structured instruction variations with sociotechnical context to expose potential model vulnerabilities. The dataset was used to evaluate three commercial LLM platforms ChatGPT using a GPT-4 class lightweight variant (OpenAI), Gemini 2.5 Flash (Google), and Claude Sonnet 4.5 (Anthropic) through controlled experiments targeting instruction manipulation in Indonesian. The results reveal distinct robustness profiles across models. Gemini 2.5 Flash exhibits moderate observed resilience, with 76% of scenarios classified as medium risk and 12% as high risk. ChatGPT demonstrates higher observed robustness under the tested scenarios, with 88% of cases classified as low risk and no high-risk outcomes. Claude Sonnet 4.5 shows intermediate observed resilience, with 72% low-risk and 28% medium-risk scenarios. High-risk cases primarily involve direct role override, urgency- or emotion-based prompts, and anti-censorship instructions, while structural ambiguities and multi-intent manipulations tend to result in medium risk, and mildly persuasive prompts fall under low risk. These findings suggest that while contemporary LLM defense mechanisms are effective against explicit attacks, contextual and emotionally framed manipulations continue to pose residual security challenges. This study contributes the first Indonesian-language prompt injection dataset and demonstrates the STAR framework as a practical and standardized approach for evaluating LLM security in digital business applications.
Security Evaluation of Indonesian LLMs for Digital Business Using STAR Prompt Injection Agnes Irene Silitonga; Irwandi, Hafiz; Silitonga, Agnes Irene; Rudy Chandra; Simamora, Windi Saputri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15662

Abstract

The adoption of Large Language Models (LLMs) in digital business systems in Indonesia is rapidly increasing; however, systematic security evaluation against Indonesian language prompt injection remains limited. This study introduces the Indonesian Prompt Injection Dataset, consisting of 50 attack scenarios constructed using the STAR framework, which combines structured instruction variations with sociotechnical context to expose potential model vulnerabilities. The dataset was used to evaluate three commercial LLM platforms ChatGPT using a GPT-4 class lightweight variant (OpenAI), Gemini 2.5 Flash (Google), and Claude Sonnet 4.5 (Anthropic) through controlled experiments targeting instruction manipulation in Indonesian. The results reveal distinct robustness profiles across models. Gemini 2.5 Flash exhibits moderate observed resilience, with 76% of scenarios classified as medium risk and 12% as high risk. ChatGPT demonstrates higher observed robustness under the tested scenarios, with 88% of cases classified as low risk and no high-risk outcomes. Claude Sonnet 4.5 shows intermediate observed resilience, with 72% low-risk and 28% medium-risk scenarios. High-risk cases primarily involve direct role override, urgency- or emotion-based prompts, and anti-censorship instructions, while structural ambiguities and multi-intent manipulations tend to result in medium risk, and mildly persuasive prompts fall under low risk. These findings suggest that while contemporary LLM defense mechanisms are effective against explicit attacks, contextual and emotionally framed manipulations continue to pose residual security challenges. This study contributes the first Indonesian-language prompt injection dataset and demonstrates the STAR framework as a practical and standardized approach for evaluating LLM security in digital business applications.
Analisis Sentimen Dalam Pemasaran Digital:Kajian Literatur Agnes Irene Silitonga; Agnes Putri Farida Sitorus; Hafiz Irwandi; Ferry Indra Sakti H. Sinaga
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 10, No 1 (2026): SEMNAS RISTEK 2026
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v10i1.8879

Abstract

A Data Mining Approach to Clustering Cases of Violence Against Children in Indonesia Agnes Irene Silitonga
International Journal of Information System and Innovative Technology Vol. 4 No. 2 (2025): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/tc9nng05

Abstract

Violence against children in Indonesia remains a crucial issue that requires a data-driven approach for more targeted interventions. This study aims to cluster provinces in Indonesia based on the number of cases of violence and the types of violence committed, namely physical, psychological, and sexual violence. The method used in this study is the K-Means Clustering algorithm, an unsupervised learning technique in data mining that is able to find hidden patterns in large data sets. Data was obtained from the Ministry of Women's Empowerment and Child Protection's Gender and Child Information System (SIGA), which covers 38 provinces. The clustering results produced three main groups: a cluster with high levels of violence consisting of the provinces of North Sumatra, DKI Jakarta, West Java, Central Java, and East Java; a cluster with moderate levels of violence consisting of 16 provinces; and a cluster with low levels of violence covering 17 provinces. These findings are expected to form the basis for the development of evidence-based child protection policies geographically and thematically.
Penerapan sistem penyiraman tanaman otomatis berbasis tenaga surya bagi petani kancang panjang di Desa Kota Datar Yoakim Simamora; Mega Silfia Dewy; Agnes Irene Silitonga; Michel Frits Immanuel
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 4 (2025): Juli
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i4.32112

Abstract

Abstrak Penerapan sistem penyiraman tanaman otomatis berbasis tenaga surya menawarkan solusi inovatif untuk meningkatkan efisiensi pertanian, khususnya bagi petani kacang hijau di Desa Kota Datar. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem penyiraman otomatis yang memanfaatkan energi surya sebagai sumber daya utama, sehingga mengurangi ketergantungan pada listrik konvensional dan biaya operasional. Sistem ini dirancang dengan komponen utama meliputi panel surya untuk menangkap energi matahari, mikrokontroler sebagai unit pengontrol utama, sensor kelembaban tanah untuk mendeteksi kondisi tanah, dan pompa air DC untuk mengalirkan air. Prinsip kerja sistem ini adalah ketika sensor mendeteksi bahwa kelembaban tanah berada di bawah ambang batas yang ditentukan untuk tanaman kacang hijau, mikrokontroler akan mengaktifkan pompa air secara otomatis. Pompa akan berhenti beroperasi setelah kelembaban tanah mencapai tingkat optimal. Keunggulan utama dari sistem ini adalah kemampuannya untuk beroperasi secara mandiri dan berkelanjutan karena didukung penuh oleh tenaga surya, menjadikannya sangat cocok untuk daerah pedesaan yang mungkin memiliki akses listrik terbatas atau biaya listrik yang tinggi..Kata kunci: penyiraman otomatis; tenaga surya; mikrokontroler; pompa air; sensor kelembapan tanah. Abstract The implementation of an automatic plant watering system powered by solar energy offers an innovative solution to improve agricultural efficiency, particularly for mung bean farmers in Kota Datar Village. This study aims to design and implement an automatic irrigation system that utilizes solar energy as its main power source, thereby reducing dependence on conventional electricity and operational costs. The system is designed with key components including solar panels to capture sunlight, a microcontroller as the main control unit, soil moisture sensors to detect soil conditions, and a DC water pump to deliver water. The working principle of this system is that when the sensor detects that soil moisture is below the threshold level set for mung bean plants, the microcontroller will automatically activate the water pump. The pump stops operating once the soil moisture reaches the optimal level. The main advantage of this system is its ability to operate independently and sustainably, as it is fully powered by solar energy, making it highly suitable for rural areas that may have limited access to electricity or face high electricity costs. Keywords: automatic watering syste; solar power; microcontroler; water pump.
V-LAMOT: A Cognitive-Load Optimized Virtual Lab for Three-Phase Motor Control Isnaini, Muhammad; Purba, Sukarman; Dewy, Mega Silfia; Solihin, Muhammad Dani; Silitonga, Agnes Irene
Journal of Educational Technology and Learning Creativity Vol. 4 No. 1 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v4i1.2766

Abstract

Purpose of the study: This study aims to design and validate V-LAMOT, a web-based virtual laboratory for three-phase motor starting simulation. The system is intended to address limitations of physical laboratories by providing an accessible and safe environment while maintaining conceptual accuracy and supporting the development of practical motor control skills. Methodology: The study adopted the Systems Development Life Cycle (SDLC) to develop the V-LAMOT platform using HTML5, CSS, JavaScript, and state-machine modeling. The design was guided by Cognitive Load Theory principles. Data were obtained through expert validation instruments and the System Usability Scale (SUS), and analyzed using Shapiro–Wilk tests, one-sample t-tests, Cohen’s d, and Pearson correlation with 30 students. Main Findings: Expert validation indicated high feasibility, with conceptual accuracy reaching a mean score of 4.50/5. SUS evaluation produced an overall score of 78.83 (“Good”), with learnability scoring highest at 82.00. All usability measures were significantly above the benchmark (p < 0.001) with large effect sizes (d > 0.8). A strong correlation between usability and learnability (r = 0.823) suggested effective cognitive load reduction. Novelty/Originality of this study: This study presents an integrated virtual laboratory that combines state-machine modeling with Cognitive Load Theory-based interface design for three-phase motor control. Unlike conventional simulations, V-LAMOT integrates multiple motor starting methods in one environment and empirically links usability, learnability, and cognitive load reduction, advancing virtual laboratory development through systematic integration of technical accuracy and pedagogical principles.
Classification of Teacher Certification Eligibility Using the C4.5 Algorithm Agnes Irene Silitonga; Mismauli Nainggolan; Tasya Arcinta; Yoakim Simamora; Ferry Indra Sakti H Sinaga
International Journal of Information System and Innovative Technology Vol. 5 No. 1 (2026): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/2ar4tf74

Abstract

Determining teacher certification eligibility is a crucial process in improving the quality of education. The C4.5 algorithm is a decision tree-based machine learning algorithm. This algorithm offers a systematic approach to data analysis and provides accurate results for decision-making. This study aims to develop a predictive model using the C4.5 algorithm to assess teacher certification eligibility based on relevant data such as teaching experience, education, and competency exam results. This study reveals that the C4.5 algorithm is capable of producing transparent decision rules and enabling clear interpretation of the results. This research is expected to make a significant contribution to supporting a more objective and efficient teacher certification policy.
Co-Authors Adi Widarma, Adi Agnes Putri Farida Sitorus Akbar Lubis, Ali Ali Akbar Lubis Anisa Fitri Annisa Fadhillah Pulungan Azmi, Aqilah Bakti Dwi Waluyo Caca Pratiwi Chintia Ni Cinta Rizkia Zahra Lubis Darwita Arini Nasution Dewi Pika Lbn Batu Elson Zega Enjelina Sinaga Erita Astrid Erni Rukmana Ferry Indra Sakti H. Sinaga Fihi Khoirani Sihotang Fritz Immanuel S, Michael Hafiz, Fahrul Al-hamdi Haryadi Haryadi - Hastuti, Pebri Hidayatullah, Amar Histi Trifesi Naibaho Irwandi, Hafiz Jufri Darma Kanaya Yori Damanik Lisa Melvi Ginting Lukas Ginting Lumban Toruan, Anjel Monika Mega Silfia Dewy Mega Silfia Dewy, Mega Silfia Michel Frits Immanuel Mismauli Nainggolan Muhammad Dani Solihin Muhammad Dani Solihin Muhammad Isnaini Muhammada Aulia Rahman S Mutia Ardila Mutiara Agustina Nst Mutiara Akbar Nasution Nerva, Souza Al-Gibrani Nurdina Safitri Panjaitan, Bon Jovi Marselino Panjaitan, Gabriella Dominggoes Prayoga, Wira Ramadhan, Taufiq Ratu Evina Dibyantini Rizwinie, Khesya Sabila Rudy Chandra Samuel Sembiring Sari, Dwy Puspita Selvia Apriani Sembiring, Muhammad Aulia Rahman Siahaan, Parlaungan Gabriel Silaban, Fanny Margaretha Simamora, Windi Saputri Situmorang, Hasianna Nopina Sukarman Purba Sukarman Purba Tasya Arcinta Teguh Fachri Fahros Thohiri, Roza Tumangger, Emya Malum Karina Tyas Permatasari Vetric Styven Silaban Yatty Destani Sandy Yoakim Simamora Zada Annuri Nabila