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METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Published by Universitas Methodist Indonesia
ISSN : 24427861     EISSN : 26143143     DOI : https://doi.org/10.46880/mtk
Core Subject : Science,
Computer Science & IT
JURNAL METHODIKA diterbitkan oleh Program Studi Teknik Informatika dan Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Methodist Indonesia Medan sebagai media untuk mempublikasikan hasil penelitian dan pemikiran kalangan Akademisi, Peneliti dan Praktisi bidang Teknik Informatika dan Sistem Informasi. Jurnal ini mempublikasikan artikel yang berhubungan dengan bidang ilmu komputer, teknik informatika dan sistem informasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 212 Documents
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EVALUATING THE EFFECTIVENESS OF DISTILBERT FOR SENTIMENT ANALYSIS OF PLAYER FEEDBACK IN GAME DEVELOPMENT Ahmad Fadhil N; Saragih, Eka Parima
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4498

Abstract

Real-time sentiment analysis (SA) plays an increasingly vital role in enhancing player experience through emotion-aware game design. By enabling systems such as dynamic difficulty adjustment, adaptive non-playable character (NPC) behavior, and responsive narrative progression, SA allows games to respond intelligently to player emotions. This study investigates the effectiveness of DistilBERT, a lightweight transformer-based language model, for multi-label emotion classification using the GoEmotions dataset, which includes 27 fine-grained emotion categories. The model’s performance was evaluated in terms of classification accuracy and computational efficiency. Experimental results reveal that DistilBERT delivers surprisingly strong performance despite its reduced size, making it a viable candidate for real-time applications in resource-constrained environments. These findings indicate that lightweight transformer models can support emotionally adaptive gameplay without significant trade-offs in latency or accuracy. Future work will focus on integrating DistilBERT into a live game environment to assess its impact on player engagement and real-time system responsiveness.
MODEL PENGUKURAN KINERJA RANTAI PASOK BERBASIS GREEN SCOR DAN FUZZY AHP: STUDI KASUS PT. ARTERIA DAYA MULIA Nurpatimah, Suci; Magdalena, Lena; Febima, Mesi
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4510

Abstract

Supply chain performance measurement plays a crucial role in supporting operational continuity and corporate competitiveness, especially in meeting the demands for efficiency, effectiveness, and environmental sustainability. Imbalances in supply chain management can lead to resource waste, environmental pollution, and decreased customer satisfaction. PT. Arteria Daya Mulia, a rope manufacturing company, currently lacks a supply chain performance measurement system that fully incorporates sustainability aspects. This study aims to design a performance measurement model based on the Green SCOR framework and the Fuzzy AHP method as a strategic decision-making tool that considers sustainability dimensions. Performance indicators were determined according to the five main Green SCOR processes (Plan, Source, Make, Deliver, and Return), comprising 14 KPIs developed through literature review and field validation. Data were collected through observations, interviews, and questionnaires, then processed using the Fuzzy AHP method to obtain the priority weight of each indicator. The results show that the total supply chain performance score is 88, calculated by combining the weights with the Snorm de Boer values. Several indicators demonstrated excellent performance with a maximum Snorm value (100). However, one critical indicator was identified with the lowest Snorm value—% Error-free Return Shipped in the Return process—scoring 0.02 with a final SCM score of 0.0008, indicating the need for immediate improvement. The developed information system also generates automatic improvement recommendations based on the measurement results. This model is expected to assist the company in monitoring, evaluating, and continuously improving supply chain performance.
IMPLEMENTASI K-MEANS RFM DAN HOLT-WINTERS EXPONENTIAL SMOOTHING ADDITIVE DALAM SISTEM BUSINESS INTELLIGENCE UNTUK STRATEGI PENGELOLAAN PELANGGAN PADA PERUSAHAAN TRANSPORTASI.: Pembuatan Dashboard BI Segmentasi pelanggan dan peramalan Jumlah pelanggan menggunakan Tools Tableau menggunakan metode Kmeans RFM dan Holtwinters Exponential Smoothing Priandini, Belfania; Marsani Asfi; Lena Magdalena
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4511

Abstract

The growth of customer data in the transportation industry drives the need for analytical systems capable of segmenting customers objectively and strategically. This study aims to apply the K-Means Clustering method based on the Recency, Frequency, and Monetary (RFM) model for customer segmentation and utilize the Holt-Winters Exponential Smoothing Additive method to forecast passenger numbers. The dataset comprises 10,440 customer transactions from PT XYZ during the 2022–2024 period. RFM values were calculated, normalized, and processed using the K-Means algorithm to produce three customer clusters: Loyal, Regular, and Passive. Subsequently, the Holt-Winters method was employed to forecast passenger numbers, achieving the smallest Mean Absolute Percentage Error (MAPE) of 6.88%, indicating a high level of accuracy. The results were visualized through an interactive dashboard using Tableau, enabling management to make data-driven decisions. This research demonstrates that integrating segmentation and forecasting methods into a Business Intelligence system can enhance the effectiveness of marketing strategies and the operational efficiency of the company.
ANALYZING LECTURER PERFORMANCE FACTORS FROM COURSE EVALUATION SURVEYS USING K-MEANS CLUSTERING AND C4.5 CLASSIFICATION alfaro, alfaro
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4628

Abstract

One of the quality of education of a college can be seen from the quality of the performance of the lecturers in the higher education tridharma namely education, research, and development and community service. This study aims to analyze the lecturer performance factors based on the course evaluation survey on the tridharma of higher education in the implementation of academic evaluations and decision making for lecturers in the Computer Science study program, Pakuan University. The research method applies a combination of two data mining methods, namely k-means clustering and C4.5 classification used in the assessment of the performance of lecturers, especially in the process of education and college teaching which includes pedagogical, professional, personality and social competencies. The results of the K-means clustering mining process were assessed by learning, namely 5 sufficient lecturer clusters, 16 good cluster lecturers and 14 excellent cluster lecturers. C 4.5 classification is used to see the connectedness of factors such as learning, publication, education, PKM, support. This study shows that publication criteria are the most influential factors in the performance assessment of lecturers. Testing the level of accuracy using the K-fold Cross Validation method with 5-fold Cross Validation is 80.00% and 7-fold Cross Validation which is 82.86%.
PREDIKSI KEHADIRAN PESERTA RAKORNAS APTIKOM MENGGUNAKAN METODE LEAST SQUARE Silaban, Cristina Adelia Putri; Manalu, Darwis Robinson; Margaretha Yohanna
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4642

Abstract

APTIKOM (Asosiasi Pendidikan Tinggi Informatika dan Komputer) merupakan asosiasi yang mewadahi Perguruan Tinggi Indonesia yang memiliki rumpun Ilmu Komputer dan Teknologi Informasi yang berperan dalam pengembangan kurikulum, standar pendidikan dan sertifikasi professional di bidang Teknologi Informasi (TI).Prediksi jumlah kehadiran peserta Rakornas APTIKOM dapat memberikan perkiraan jumlah peserta di tahun selanjutnya untuk memudahkan panitia penyelenggara Rakornas dalam merencanakan kegiatan secara lebih terukur dan berbasis data dengan menggunakan metode Least Square. Hasil prediksi dianalisis dan dikategorikan menjadi tingkat keaktifan berdasarkan frekuensi kehadiran peserta. Hasil evaluasi model menunjukkan bahwa metode Least Square dapat digunakan secara efektif untuk memprediksi pola kepesertaan dan menghasilkan analisis kategori yang bermanfaat sebagai dasar pengambilan keputusan oleh pihak APTIKOM.
FEW-SHOT LEARNING FOR AML CELL CLASSIFICATION USING PROTOTYPICAL NETWORKS Dirgayussa, I Gde Eka; Herman, Kevin Elfancyus; Nugroho, Doni Bowo; Sekar Asri Tresnaningtyas; Meita Mahardianti; Nurul Maulidiyah; Rafli Filano; Rudi Setiawan; Muhammad Artha Jabatsudewa Maras; Yohanssen Pratama
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4650

Abstract

Accurate blood cell classification is crucial for diagnosing Acute Myeloid Leukemia (AML) but limited medical data poses challenges for traditional machine learning models. This study presents a Few-Shot Learning (FSL) framework utilizing a Prototypical Network architecture with a ResNet-34 backbone to classify AML blood cell types from microscopic images. In this study, we utilize datasets consisting of 15 morphologically distinct cell classes. A 15-way, 5-shot, 5-query episodic setup was adopted to simulate data-scarce conditions. Evaluation via 5-fold cross-validation yielded strong performance, with an average accuracy of 97.76%, precision of 98.78%, recall of 96.55%, and F1-score of 97.76%. FSL training times were consistent (4.22–4.26 minutes per fold), and t-SNE along with confusion matrices confirmed the model’s ability to distinguish similar cell types. To validate the approach, its performance was compared with a conventional supervised CNN using the same ResNet-34 backbone. The FSL model outperformed the CNN across all metrics such as accuracy (98.32% vs. 77.25%), precision (98.55% vs. 76.87%), recall (98.31% vs. 78.66%), and F1-score (98.33% vs. 75.26%), while also requiring far less training time (~4.24 min/fold vs. ~420 min total). These results highlight the promise of FSL based methods for accurate, efficient, and scalable hematologic diagnostics in data limited settings.
ANALISIS SENTIMEN MASYARAKAT TERHADAP UU PERLINDUNGAN DATA PRIBADI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Rayhan Abdul Jabbar Fahmi; Wahib Muhibi Nur; Dee Canawine; Muhammad Naufal Kusumajaya; Ahmad Faris Fadhlillah; Nur Aini Rakhmawati
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i1.2335

Abstract

Machine Learning berperan penting dalam menangani masalah klasifikasi dan pemrosesan untuk memprediksi perkembangan informasi terkait Undang-Undang Perlindungan Data Pribadi. Data pribadi merupakan informasi individu yang harus dijaga integritasnya, dengan perlindungan yang dijamin oleh negara. Namun sayangnya, Indonesia menduduki peringkat sangat rendah dalam hal keamanan siber dibandingkan negara-negara lain. Penelitian ini bertujuan untuk menggali tantangan ini dan mencari solusi potensial untuk memastikan keamanan dan perlindungan data pribadi. Dalam penelitian ini, metode Support Vector Machine (SVM) digunakan untuk menganalisis dan mengkategorikan sentimen masyarakat terkait Undang-Undang Perlindungan Data Pribadi pada platform aplikasi X bersifat positif, netral, atau negatif. Data sampel yang digunakan sebanyak 275 data tweets yang kemudian dilakukan scraping. Pengolahannya menggunakan pemrograman Python dan tools Google Colab. Sebelum dilakukan analisis, terlebih dahulu dilakukan preprocessing untuk menghilangkan kata-kata maupun informasi yang tidak diperlukan sehingga tingkat akurasi yang dihasilkan dapat mendekati gambaran pada kenyataannya. Setelah dilakukan analisis, diperoleh hasil sentimen positif sebanyak 83 data tweets, sentimen negatif sebanyak 43 dan sentimen bersifat netral sebanyak 143. Hasil pengujian klasifikasi pada data tweets memiliki akurasi sebesar 73%. Dengan menggunakan SVM, diharapkan dapat mengidentifikasi persepsi dan respon masyarakat terhadap perlindungan data pribadi serta mengembangkan strategi yang lebih efektif untuk menjaga keamanan data pribadi di Indonesia. Upaya ini penting mengingat tantangan meningkatnya ancaman siber dan perlunya perlindungan data yang kuat dalam era digital saat ini.
SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN KUALITAS DEPOT AIR MINERAL ISI ULANG MENGGUNAKAN METODE TOPSIS (TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION) Febianus Asa; Elisabeth Kolastriwan Romanda; Jekonia Nelchika Titing; Maria Claris Salzano Nurak; Pua geno, Muhamad Nazhif Zuhri; Yampi R. Kaesmetan
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i1.2439

Abstract

The Decision Support System (DSS) for determining the quality of mineral water depots using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is an application designed to assist mineral water depot managers in selecting the best mineral water supplier based on certain criteria. The TOPSIS method is used to solve multi-criteria problems by considering the relative proximity to ideal solutions and anti-ideal solutions.First, relevant criteria for assessing the quality of mineral water are selected, including physical, chemical and microbiological parameters. Then, mineral water quality data from various suppliers is processed and normalized. Next, the normalized decision matrix is used to calculate the ideal solution and anti-ideal solution matrices. After that, a relative closeness score for each supplier is calculated based on the Euclidean distance to the ideal and anti-ideal solutions.The results of the TOPSIS analysis are used to provide recommendations for the best mineral water suppliers. By using this system, mineral water depot managers can optimize supplier selection based on predetermined quality criteria, thereby increasing customer satisfaction and maintaining the reputation of the mineral water depot in the market.
PERBANDINGAN ALGORITMA SIMPLE LINEAR REGRESSION DAN SUPPORT VECTOR REGRESSION DALAM PREDIKSI JUMLAH PENDUDUK DI SULAWESI TENGGARA Rafi Iyad Madani Chaidir; Ahmad Fadli Ramadhan; Hashimatul Zaria; Rizal Adi Saputra
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i1.2548

Abstract

Pertumbuhan penduduk di Sulawesi Tenggara terus meningkat, mencapai kenaikan sekitar 8% dari tahun 2014 hingga 2023, seperti yang tercatat dalam data Badan Pusat Statistika (BPS). Dampak potensial dari fenomena ini pada kehidupan masyarakat dan pembangunan wilayah perlu menjadi perhatian. Dengan memprediksi jumlah penduduk, dapat membantu dalam perencanaan pembangunan jangka panjang, pengembangan infrastruktur, dan pengelolaan dan alokasi sumber daya. Penelitian ini bertujuan untuk menciptakan model yang efektif untuk meramalkan pertumbuhan penduduk dengan akurasi yang baik. Data yang digunakan adalah data jumlah penduduk Sulawesi Tenggara dari tahun 2014 hingga 2023 menurut kabupaten/kota oleh BPS. Prediksi dilakukan menggunakan metode Support Vector Regression (SVR) dan Simple Linear Regression (SLR). Hasil perbandingan menunjukkan bahwa SLR menunjukkan performa yang lebih baik secara umum dibandingkan dengan SVR pada sebagian besar model, dengan memiliki rata-rata MAPE sebesar 1.89% dan RMSE sebesar 0.51%. Temuan ini mengonfirmasi bahwa SLR merupakan algoritma yang lebih akurat dalam meramalkan pertumbuhan penduduk di Sulawesi Tenggara.
SISTEM INFORMASI PENDAFTARAN SERTIFIKASI KOMPETENSI KEAHLIAN PADA ASTEKINDO KUBU RAYA Wulandari, Septi Novita; Daning Nur Sulistyow
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i1.2549

Abstract

ASTEKINDO merupakan perkumpulan dari para professional atau para pekerja yang bergerak dibidang jasa konstruksi dan terakreditasi Lembaga Pengembangan Jasa Konstruksi Nasional, pada proses pendaftaran uji komptensi pada ASTEKINDO masih menerapkan pendaftaran secara konvensional yaitu pemohon diminta untuk datang ke ASTEKINDO untuk mengisi formulir, melampirkan persyaratan dan melakukan pembayaran. Sistem yang berjalan saat ini sangat beresiko terjadi kesalahan dalam pengisian data pemohon karena masih menggunakan penulisan tangan pada setiap pengajuan, sering kali pemohon lupa membawa berkas persyaratan dan menunda pendaftaran. Penelitian ini mengembangkan sebuah website pendaftaran agar bisa dijadikan referensi untuk beralihnya sistem yang masih konvensional menjadi sistem berbasis website. Website ini dikembangkan menggunakan metode Waterfall dan mengimplementasikan framework CodeIgniter. Berdasarkan hasil analisa kebutuhan didapatkan 3 level akses yang terdiri dari Ketua, Admin dan Pemohon. Dengan menerapkan sistem ini diharapkan memudahkan Pemohon saat melakukan pendaftaran karena tidak lagi terbatas oleh jarak dan waktu juga memudahkan pemohon dalam pengisian formulir.

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