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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi JURNAL DIAN MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
MY. Teguh Sulistyono
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Peningkatan Usability Point of Sales (PoS) Berbasis Human Centered Design (HCD) Agus Hermanto; Nur Sela Ameiliawati; Agustinus Bimo Gumelar; Lukman Junaedi; Agung Widodo; MY Teguh Sulistyono; Achmad Teguh Wibowo
JOINS (Journal of Information System) Vol 7 No 1 (2022): Edisi Mei 2022
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2175.084 KB) | DOI: 10.33633/joins.v7i1.5528

Abstract

Dalam perkembangan teknologi transaksi online hingga fasilitas e-commerce, sistem point-of-sale (POS) menjadi sangat populer karena menyediakan cara transaksi yang cepat dan nyaman untuk bisnis. Sistem ini mampu mengakomodasi tugas-tugas vital seperti transaksi online, keamanan, integrasi dengan perpajakan, hingga laporan manajemen. Oleh karena itu, menjadi penting dalam memastikan kualitas perangkat lunak dan pemanfaatan fungsi bisnis yang efektif. Di antara beberapa fungsi dan atribut kualitas perangkat lunak, sifat kegunaan perangkat lunak POS sangatlah krusial, karena antarmuka pengguna secara langsung sangat terkait dengan perilaku kasir, kepuasan pelanggan, dan keuntungan pasar. Namun, pelaksanaan evaluasi kegunaan sistem transaksi yang menggunakan teknologi POS tidak mudah karena secara umum ditampilkan banyak konfigurasi, dan antarmuka yang kompleks. Banyak model kualitas yang tersedia belum cukup untuk mengevaluasi kegunaan sistem POS yang hanya mencakup sebagian tampilan fungsinya.Dalam penelitian ini, kami melakukan investigasi sepuluh model kualitas dari metode System Usability Scale (SUS) dan mengekstrak faktor terkait kegunaan dari masing-masing model dan mengimplementasikannya berbasis desain yang berfokus pada manusia atau Human Centered Design (HCD). Evaluasi dari penggunaan aplikasi POS yang dibuat menghasilkan nilai rata-rata skor SUS sebesar 78,2 yang menunjukkan tingkat penerimaan masuk kategori Baik (Good).
Pemanfaatan Sumberdaya IKM dalam Meningkatkan Produktivitas dan Pemasaran Produk Melalui Transformasi Digital Masyarakat Desa MY Teguh Sulistyono; Wellia Shinta Sari; Siti Hadiati Nugraini; Maulana Zaky Muhammad; Richard Emmerig
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 14, No 2 (2023): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v14i2.11874

Abstract

Perguruan Tinggi adalah salah satu lembaga pendidikan yang membatu dan berperan aktif di dalam mencerdaskan kehidupan bangsa dengan mentransfer ilmu pengetahuan atau knowledge transfer kepada lembaga-lembaga pendidikan lain, lembaga-lembaga lain baik yang setara ataupun yang berada di bawahnya, baik pendidikan formal atau non formal. Salah satu industri yang membutuhkan knowledge transfer adalah Industri Kecil Menengah (IKM). IKM yang dijadikan mitra adalah IKM milik Ibu Siti Rokhanah yang berlokasi di Kelurahan Nawangsari Kecamatan Weleri Kabupaten Kendal. Berjarak kurang lebih 60 km dari Universitas Dian Nuswantoro Semarang. IKM katering dan snak (kue tradisional) yang mengalami masalah yaitu masalah pemasaran produk, produksi katering dan snack (kue tradisional), masalah manajemen usaha dan masalah manajemen keuangan. Masalah pemasaran produk terjadi karena dilakukan dari mulut ke mulut sehingga target penjualan tidak tercapai, masalah produksi terjadi karena masih menggunakan alat memasak tradisional dan tidak adanya oven yang digunakan dalam produksi sehingga pemanasan yang tidak merata dan proses produksinya berulang-ulang, masalah manajemen usaha tidak adanya pengelolaan sumberdaya yang baik untuk jalannya sebuah industri dan masalah manajemen keuangan tidak adanya pencatatan keluar masuk keuangan sehingga tidak diketahui rugi dan labanya. Untuk membantu mengatasi keempat masalah tersebut maka diusulkan dengan memperbaiki proses pemasaran, proses produksi, proses manajemen usaha dan proses manajemen keuangan. Untuk memperbaiki keempat proses tersebut yaitu untuk pemasaran diadakan pemasaran melalui media internet, brosur, MMT dan media sosial,  untuk manajemen usaha dan manajemen keuangan dilakukan pelatihan manajemen keuangan, manajemen usaha dan pelatihan pemasangan iklan di internet dan media sosial, sedangkan untuk proses produksi pembuatan alat masak dengan penggunaan oven otomatis dengan panas merata. Hasil akhir dari Program Kemitraan Masyarakat ini adalah pemanfaatan sumberdaya Industri Kecil Menengah (IKM) dalam meningkatkan produktivitas dan pemasaran produk melalui transformasi digital masyarakat desa.
SISTEM PENGAMBILAN KEPUTUSAN PENGGUNAAN TEKNOLOGI INFORMASI TRANSFORMASI DIGITAL UNTUK PEMILIHAN PEMASARAN PRODUK MELALUI MEDIA SOSIAL DENGAN MENGGUNAKAN METODE WEIGHTED PRODUCT MY Teguh Sulistyono
Prosiding Sains Nasional dan Teknologi Vol 13, No 1 (2023): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 2023
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v13i1.9129

Abstract

Penerapan teknologi informasi  pada UMKM, IKM dan UKM membutuhkan peran teknologi informasi untuk transformasi digital melalui media sosial. Yang menjadi permasalahan adalah untuk menentukan media sosial yang tepat dalam memasarkan produk membutuhkan rekomendasi melalui penelitian yang datanya diambil melalui resonden.  Agar tujuan UMKM, IKM atau UKM tercapai perlu adanya rekomendasi dari pihak-pihak lain seperti akademisi untuk membantu agar pemasaran produk UMKM, IKM atau UKM dapat laku dipasaran dengan pemilihan media yang tepat.Metode yang digunakan dalam penelitian ini menggunakan metode Weighted Product yang merupakan metode untuk menyelesaikan Multi Attribute Decision Making (MADM), dengan menggunakan 4 tahapan yaitu menentukan kriteria, menentukan rating kecocokan, menentukan nilai V dan menentukan alternative terbaik.Hasil akhir dari penelitian ini adalah rekomendasi system pengambilan keputusan penggunaan teknologi informasi  transformasi digital  untuk pemilihan pemasaran produk melalui media sosial dengan Menggunakan Metode  Weighted Product.
PENYARINGAN NOISE MELALUI BAND PASS FILTER BERBASIS SINYAL EEG SEBAGAI LANGKAH AWAL PENGOLAHAN DATA DALAM PENGAMBILAN KEPUTUSAN REABILITASI MEDIS PASIEN STROKE MY Teguh Sulistyono
Prosiding Sains Nasional dan Teknologi Vol 13, No 1 (2023): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 2023
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v13i1.9137

Abstract

Elektroensefalografi (EEG) akan menghasilkan sebuah informasi perekaman secara non-invasif dari sinyal otak yang digunakan untuk menganalisis aktivitas kinera otak yang diperuntukkan tenaga medis ataupun klinis dalam mendiagnose, yang setelah itu dilakukan proses lanjut yaitu rehabilitasi dan monitoring terhadap pasien sebagai sarana kesembuhan penyakit stroke. Sinyal EEG yang sudah direkam merupakan sinyal asli gelombang otak yang didalamnya masih terdapat noise artefak atau kontaminasi berbagai sinyal. Noise Artefak tersebut antara lain teradi karena pergerakan mata atau Artefak Electrooculography. Pada paper yang menjadi permasalahan utama adalah dalam menginterpretasi sinyal EEG masih terdapat noise sehingga perlu melakukan denoising artefak sinyal EOG menggunakan metode High Low Pass Filter dan FIR Filter. Memalui metode memalui eksperimen diharapkan suatu hasil yang nantinya akan memeprlihatkan berkurangnya seminimal mungkin noise pada sinyal EEG.
Clustering and Profiling Analysis for FIFA Football Player using K-Means Azzami, Salman Yuris Adila; Hadi, Heru Pramono; Alzami, Farrikh; Irawan, Candra; Nurhindarto, Aris; Sulistyono, MY Teguh
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 1 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i1.7897

Abstract

The selection of football players is a complex process involving talent evaluation based on various performance indicators, combining objective measures with subjective assessments by coaches and scouts. This research aims to improve the football player selection process using the K-Means clustering method based on the attributes of transfer price, performance, body specifications, position, and player ability. The dataset used consists of 17.947 players taken from the FIFA 19 edition of the soFIFA.com platform, which includes complete information such as transfer price, performance, body specifications, position, and player ability. The data was processed using principal component analysis (PCA) to reduce the dimensions, followed by the Elbow Method to determine the optimal number of clusters. The clustering results show the distribution of players based on their on-field roles, such as center back, goalkeeper, striker, and left wing back. The profiling of players from each cluster is identified based on position, body type, dominant foot usage, transfer price, and rating. This research provides useful insights for coaches and scouts in selecting players that suit specific roles in the team using better analysis. The findings also highlight the importance of player clustering for data-driven decision-making, which can optimize team composition and overall performance.
AGGLOMERATIVE HIERARCHICAL CLUSTERING FOR REGIONAL GROUPING IN CENTRAL JAVA BASED ON WELFARE INDICES Kurnia Desita, Raafi; Fahmi, Amiq; Rohmani, Asih; Sulistyono, MY. Teguh
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6445

Abstract

Central Java Province comprises 35 regencies/cities with diverse welfare characteristics. These variations present challenges for the government in formulating targeted development policies. This study aims to group regions in Central Java based on welfare indices to support more effective policy planning. The Agglomerative Hierarchical Clustering method with the Average Linkage approach is applied to cluster the regions based on three attributes: Human Development Index, Uninhabitable Houses, and Economic Growth Rate. Data were obtained from the Central Java Provincial Social Service and the official website of the Central Statistics Agency (BPS) and processed using the proposed method. Experimental results indicate three clusters with proportions: 32 regions in cluster 1 (91.4%), 2 regions in cluster 2 (5.7%), and 1 region in cluster 3 (2.9%). Regions with higher welfare dominate the first cluster, while the second and third clusters include regions facing more significant welfare challenges. Clustering results were evaluated using the Silhouette Score (0.535) and Davies-Bouldin Index Score (0.610), demonstrating that the applied method effectively grouped regions based on the specified attributes. The findings of this study are anticipated to lay the groundwork for more directed and effective development policies.
Proboboost: A Hybrid Model for Sentiment Analysis of Kitabisa Reviews Prasetya, Rakan Shafy; Fahmi, Amiq; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11138

Abstract

The rapid advancement of digital technology has significantly transformed public behavior in social activities, particularly in online donations and zakat payments. The Kitabisa application was selected in this study not only for its popularity but also due to its high user engagement and large volume of reviews on the Google Play Store, making it an ideal representation of public trust in Indonesia’s digital philanthropy ecosystem. This research aims to analyze user sentiment toward the Kitabisa application using a hybrid Proboboost model, which combines Multinomial Naive Bayes (MNB) and Gradient Boosting Classifier through a soft voting mechanism. The model is designed to address class imbalance and improve accuracy in short-text sentiment analysis for the Indonesian language. The study employed preprocessing techniques including case folding, text cleaning, stopword removal, and stemming using the Sastrawi algorithm. Feature extraction was performed using TF-IDF, with an 80:20 train-test split and 5-fold cross-validation to ensure model reliability. Experimental results indicate that the Proboboost model achieved an accuracy of 89.51% and an F1-score of 87.4%, outperforming the Naive Bayes baseline with 87.98% accuracy. The sentiment distribution demonstrates a dominance of positive sentiment (87.24%), followed by negative (8.53%) and neutral (4.23%) reviews. These findings suggest that users generally express satisfaction and trust toward the Kitabisa platform. The results also confirm that the hybrid Proboboost model effectively balances classification performance between majority and minority sentiment classes, offering deeper insights into user perceptions of digital philanthropic services.
Comparison of Sarima and Exponential Smoothing Methods in Forecasting Exchange Rates for Farmers in Central Java Province Sulistyono, MY Teguh; Annabil, Muhammad Naufal
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11396

Abstract

This study compares the performance of the SARIMA and Exponential Smoothing (Holt-Winters) models in forecasting the Farmer Exchange Rate (NTP) for Central Java Province from 2016 to 2025. The monthly statistical data used was obtained from the Central Java Provincial Statistics Agency. The models were evaluated using MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) on test data for the period January 2016 to September 2025, while forecasting was carried out from October 2025 to December 2027. The results show that the SARIMA (1,1,1) (1,1,1,12) model has an MAE of 6.94 and an RMSE of 7.88, indicating that the model can make accurate predictions with few errors. However, the Exponential Smoothing model has a lower MAE and RMSE, implying that this model is more accurate in forecasting long-term NTP. Both models show comparable seasonal trends, with Exponential Smoothing being more stable and sensitive to seasonal changes.  This study also proposes the use of alternative forecasting approaches, such as ARIMAX, VAR, or machine learning to improve the accuracy of future forecasts.  The results of this study can be used to develop agricultural policies that maintain food price stability, improve farmer welfare, and predict future inflation fluctuations.
Comparison of Multiple Linear Regression and Random Forest Methods for Predicting National Rice Production in Indonesia Nur Cahyo, Sefrico Aji; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11398

Abstract

Rice is a strategic commodity that plays an important role in maintaining national food security. However, rice production in Indonesia still fluctuates due to variations in harvest area, productivity, climate conditions, and differences in regional characteristics. This condition demands a predictive model capable of providing more accurate production estimates to support food policy planning. This research aims to predict national rice production by comparing two methods: Multiple Linear Regression and Random Forest Regression, using data from the Central Bureau of Statistics (BPS) and Nasa Power for the period 2018–2024. The analysis stages include data preprocessing, data exploration, categorical variable transformation, splitting data into training and testing sets, model training, and evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The research results show that harvested area is the most dominant factor influencing rice production, followed by productivity, year, and province. Based on the evaluation results, Random Forest provided the best performance with an MAE value of 40,599.94, an RMSE of 77,153.07, and an R² of 0.9991. The low error value and the proximity of the prediction to the actual data indicate that this model is better at capturing non-linear patterns and inter-regional variations compared to Multiple Linear Regression. Overall, Random Forest can be an effective method for predicting national rice production and can be further developed in subsequent research by incorporating climate variables or other external factors.
Exploring Public Opinion on the 'Makan Bergizi Gratis' Program on X: A Comparative Analysis of IndoBERT-Large and NusaBERT-Large Models Arunia, Aurelya Prameswari; Sani, Ramadhan Rakhmat; Dewi, Ika Novita; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11757

Abstract

Program Makan Bergizi Gratis (MBG) has triggered extensive discourse on social media platform X, which serves as a primary space for public expression of opinions toward government policies. This study aims to analyze public sentiment toward the MBG program while simultaneously comparing the performance of two prominent Transformer-based models, namely IndoBERT-Large and NusaBERT-Large. This research adopts a quantitative approach employing supervised learning on 10,201 Indonesian-language posts (tweets) collected through web scraping from February 2024 to September 2025. A total of 2,000 samples were manually annotated as ground truth, achieving a high level of inter-annotator reliability (Cohen’s Kappa, κ = 0.81). The experimental results indicate that IndoBERT-Large outperforms NusaBERT-Large, achieving an accuracy of 83.00%, while NusaBERT-Large demonstrates competitive performance with an accuracy of 80.50%. Substantively, public discourse is dominated by negative sentiment, accounting for nearly 50% of the total data, reflecting public concerns regarding budgetary constraints and technical implementation issues. Positive sentiment ranges between 33% and 36%, indicating sustained and substantial public support for the program. These findings confirm the effectiveness of Transformer-based models in accurately capturing the dynamics of public opinion toward government policies using social media data.
Co-Authors Achmad Teguh Wibowo Adi, Derry Pramono Agung Widodo Agus Hermanto Agustinus Bimo Gumelar Aisyiyah Tri Ratna Dewi Alvino Radyo Danisworo Alzami, Farrikh Amartya Anugrah Indrawati Lestari Annabil, Muhammad Naufal Arunia, Aurelya Prameswari Asih Rohmani Asih Rohmani, Asih Azzami, Salman Yuris Adila Belinda Elsa Aulia Sinatrya Candra Irawan David Wahyu Setyo Aji Davin Hernanda Putra Dea Adwitiya Daffa Dewi Eldiana Zahra Dyah Ernawati Fahmi Amiq Hadi, Heru Pramono Heru Lestiawan Ika Novita Dewi Iklima Madina Rachmawati Indra Gamayanto Junaedi, Lukman Kurnia Desita, Raafi Maulana Zaky Muhammad MG Catur Yuantari Niken Silvia Anggraini Nugroho, Via Ardianto Nur Cahyo, Sefrico Aji Nur Sela Ameiliawati Nurhindarto, Aris Nurhindarto, Aris Prasetya, Rakan Shafy Ramadhan Rakhmat Sani Richard Emmerig S. Hadiati Nugraini Selvi Wijayanti Siti Hadiati Nugraini Stalina Anggraeny Dewi Amodia Sudaryanto Sudaryanto Sudaryanto Sudaryanto Wahibullah, Wahibullah Wellia Shinta Sari Yanuar Anggara Firdaus