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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika Bulletin of Electrical Engineering and Informatics Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Biomedical Science and Bioengineering Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Informatika
Adi Wibowo
Departement Of Computer Science, Informatics, Universitas Diponegoro
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Combination of Matrix Simple Additive Weighting Algorithm (SAW) on the Reference URICA-Scale to Measure Readiness for Change in Narcotic Rehabilitation Patients Soni Adiyono; Rahmat Gernowo; Adi Wibowo
Jurnal Aisyah : Jurnal Ilmu Kesehatan Vol 7, No S1 (2022): Suplement 1
Publisher : Universitas Aisyah Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (775.939 KB) | DOI: 10.30604/jika.v7iS1.1118

Abstract

This study aims to design a matrix combination in the URICA-Scale calculation and the simple additive weighting (SAW) method that can be used as a measuring tool to evaluate the readiness of drug rehabilitation patients using the University of Rhode Change Assessment Scale (URICA-Scale) as a desire combined with simple additive weighting (SAW) method in order to facilitate the design of electronic information systems regarding assessment tests with reference to URICA-Scale. In software development, the design modeling step in the system is one part of the (System Development Life Cycle) contained in the Waterfall model. The results of this study are able to provide an arrangement of calculation matrices where the combination of these matrices contributes to programmers in implementing them into certain programming languages. Abstrak: Penelitian ini bertujuan untuk merancang desain kombinasi matriks pada perhitungan URICA-Scale dan metode simple additive weighting (SAW) yang dapat digunakan sebagai alat ukur guna mengevauasi tentang kesiapan pasien rehabilitasi narkotika dengan menggunakan University of Rhode Change Assesment Scale (URICA-Scale) sebagai acuan yang dikombinasikan dengan metode simple additive weighting (SAW) agar dapat mempermudah dalam merancang sistem informasi elektronik mengenai tes asesmen dengan acuan URICA-Scale. Dalam pengembangan perangkat lunak langkah pemodelan desain pada sistem merupakan salah satu bagian dari (System Development Life Cycle) yang terdapat pada model Waterfall. Hasil penelitian ini mampu memberikan susunan matrix perhitungan dimana dengan adanya gabungan dari matriks tersebut memberikan kontribusi bagi programmer dalam melakukan implementasi kedalam Bahasa pemrograman tertentu.
Analisis Pengaruh Pemilihan Jumlah Variabel Linguistik Membership Function pada Metode Fuzzy Simple Additive Weighting (FSAW) untuk Perankingan Penerimaan Beasiswa Bagi Siswa Kurang Mampu (Studi Kasus : Sekolah Dasar Negeri Petompon 02 Semarang) Alfania Sarah Handayani; Adi Wibowo
Jurnal Masyarakat Informatika Vol 12, No 1 (2021): May 2021
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.12.1.41019

Abstract

SDN Petompon 02 Semarang merupakan salah satu Sekolah Dasar Negeri yang memiliki program beasiswa "Bumbung  Kemanusiaan"  yang ditujukan bagi siswa yang kurang  mampu. Oleh karena itu diperlukan perankingan siswa untuk memilih calon penerima beasiswa. Pengumpulan data untuk penelitian  ini  didapatkan melalui  wawancara  dengan  Kepala  Sekolah  SDN  Petompon  02  Semarang  untuk  mendapatkan  kriteria penerimaan beasiswa. Terdapat 5 kriteria untuk penerimaan beasiswa yaitu kepemilikan kartu miskin, rata-rata raport semester terakhir, kepemilikan piagam, penghasilan orang tua, dan tanggungan orang tua. Permasalahan ini merupakan permasalahan dunia nyata, sehingga data yang dikumpulkan biasanya melibatkan beberapa jenis ketidakpastian. Salah satu solusi dalam pengambilan keputusannya adalah memodelkan dengan fuzzy. Di dalam Metode Fuzzy Simple Additive Weighting terdapat pemilihan variabel linguistik membership function, dimana skala tersebut sangat berpengaruh  bagi perhitungan pada  metode.  Penelitian  ini bertujuan untuk  menganalisis pemilihan  jumlah variabel linguistik  membership  function  untuk mendapatkan nilai preferensi yang  sesuai dan nilai akurasi. Penelitian ini melakukan 2 percobaan yaitu dengan memilih jumlah variabel linguistik 5 dan jumlah variabel linguistik  7.  Hasil  penelitian  menunjukkan  nilai  akurasi  yang  lebih  baik  pada  pemilihan  jumlah  variabel linguistik 7 menggunakan metode Fuzzy Simple Additive Weighting mencapai 96,08%.
Evaluasi Usability pada Aplikasi Sistem Pencatatan Pegawai Menggunakan Metode Usability Testing dan USE Questionnaire Abraham Timotius Asmoro Putro; Adi Wibowo; Sutikno Sutikno
Jurnal Masyarakat Informatika Vol 15, No 2 (2024): November 2024
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.15.2.67263

Abstract

In the era of increasingly rapid digitalization, employee recording system applications have become an unavoidable necessity for companies for the efficiency and effectiveness of human resource management. In this context, the usability or usefulness of employee management system applications is a crucial factor in ensuring that users can smoothly and efficiently utilize the system provided. Usability is an important aspect in application design that is often overlooked. The success of an application is not only determined by its features and functionality, but also by the ease of use and user satisfaction in operating it. Therefore, usability evaluation is a very necessary step to ensure that the application being developed meets user needs optimally. The usability testing method and the use of questionnaires are common approaches used in evaluating the usability of an application. In the context of the CV. Cupang Semarangan employee recording system application, this study aims to evaluate the usability of the application using the usability testing method and questionnaire. This study resulted in usability values for the aspects of effectiveness, efficiency, ease of use, ease of learning, and satisfaction being 87.96%, 77.47%, 64.88%, 71.43%, and 68.57% respectively.
Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application Eka Rahmawati; Adi Wibowo; Budi Warsito
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Supporting children’s early development requires consistent attention, ensuring their growth aligns with health standards. PrimaKu is one of the mobile applications developed by the Indonesian Pediatric Society. That application was created to assist parents in recording developmental milestones, monitoring immunization schedules, and accessing practical health information. This study investigates user perceptions of the application by analyzing publicly available reviews and ratings from the Google Play Store. Four supervised machine learning algorithms were applied to classify the sentiment expressed in the reviews: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. Among the models tested, SVM achieved the highest classification accuracy (81%), followed by Random Forest (77%), Decision Tree (74%), and Naive Bayes (73%). Precision, recall, and F1-score were also used to evaluate the performance of each model. The results highlight the relevance of machine learning in capturing and interpreting user sentiment toward digital health tools. Further exploration of deep learning architectures is encouraged to enhance classification accuracy and understanding of features.
Klasifikasi Citra Sentinel melalui Google Earth Engine dengan menggunakan algoritma Machine Learning XGBoost Gregorius Anung Hanindito; Adi Wibowo; Budi Warsito
InComTech : Jurnal Telekomunikasi dan Komputer Vol. 16 No. 1 (2026)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v16i1.31354

Abstract

Remote sensing technology and Geographic Information Systems (GIS) have rapidly evolved to provide extensive data and information on land cover. This study aims to monitor land cover in Tanjung Keluang Nature Tourism Park (TWA) and its surroundings using Sentinel satellite imagery on the Google Earth Engine (GEE) platform, employing the XGBoost machine learning algorithm. The methods involved acquiring Sentinel satellite imagery, pre-processing for geometric correction, developing training and testing datasets, as well as performing classification and accuracy evaluation. The results indicate that the XGBoost algorithm can classify land cover into several categories with an accuracy of up to 98%. The classified land cover includes water bodies (23,346 Ha), open land (9,680.54 Ha), sand mining areas (931.15 Ha), and vegetation (16,596.84 Ha). This study contributes positively to the management of conservation areas, particularly in supporting decision-making for TWA Tanjung Keluang in the future.
Separable Convolutional Hierarchical Decomposition for Lightweight Residential Load Forecasting in Smart Grids Satriawan Rasyid Purnama; Henri Tantyoko; Adi Wibowo; Yesaya Rudolf Susanto Widyanto
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2869

Abstract

Residential load forecasting is essential for maintaining grid stability and energy management in smart grids. However, achieving accurate real-time forecasting under resource constraints remains challenging because Transformer and LSTM models can be computationally demanding, while lightweight linear models such as DLinear have limited modeling flexibility. This study investigates whether a hierarchical separable convolutional framework can provide accurate and efficient residential load forecasting. To address this, SeparableCLF, a lightweight hierarchical decomposition model using depthwise separable convolution, is proposed and evaluated on hourly OpenEI residential load data from 20 U.S. states (2012) at forecast horizons of 6, 12, 24, 48, and 96 h. Relative to DLinear, SeparableCLF reduced MAPE by 0.93, 1.54, and 0.79 percentage points at 24, 48, and 96 h, respectively while requiring substantially fewer parameters than Transformer and LSTM models.SeparableCLF achieved the lowest MAPE at 12, 24, and 48 h. DLinear achieved the lowest errors at 6 h, whereas LSTM achieved the lowest MAPE at 96 h; at 96 h, SeparableCLF retained the lowest MAE, MSE, and RMSE among the compared models, indicating suitability for real-time forecasting on smart meters and edge-based smart grid devices.
Multitask deep learning for sentiment analysis with sarcasm detection in bilingual code-mixed social media content Mohd Suhairi Md Suhaimin; Adi Wibowo; Ervin Gubin Moung; Patricia Anthony; Mohd Hanafi Ahmad Hijazi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10935

Abstract

Sentiment analysis in social media often hindered by sarcasm, which can reverse text meaning, and bilingual code-mixing, which adds complexity in non-English primary context. Existing approaches extract separate features for each language and translate them into a single language, resulting in the loss of contextual meaning and omission of crucial features. This paper proposes a multitask learning model for sentiment analysis with sarcasm detection tailored to bilingual code-mixed social media content. A hybrid feature engineering technique is integrated into a multitask deep learning architecture designed to capture the nuances of sentiment and sarcasm while addressing the complexities of processing bilingual code-mixed content. The hybrid technique combines domain-knowledge-based natural language processing (NLP) with a deep learning-based embedding approach. It includes rule-based preprocessing, normalization, spellchecking, feature extraction and selection, and feature representation. The engineered features are integrated into a multitask deep learning network using bidirectional long short-term memory (Bi-LSTM) combined with gated recurrent units (GRU). Using a public dataset that contains bilingual code-mixed social media content related to public security, our proposed model achieved a higher F1score compared to two baseline models that employ single task and multitask approaches.
Co-Authors Abraham Timotius Asmoro Putro Alfania Sarah Handayani Asep Insani Bayu Surarso Beta Noranita Beta Noranita Boko Nurdiyanto S. Budi Warsito Budi Warsito Cahyo Adhi Hartanto Dedy Kurniadi Djalal Er Riyanto Eka Rahmawati Eko Adi Sarwoko Ervin Gubin Moung Ferda Ernawan Fra Siskus Dian Arianto Gregorius Anung Hanindito Guruh Aryotejo Henri Tantyoko Iffa Zainan Nisa Indra Jaya Indra Waspada Kabul Kurniawan Mohd Hanafi Ahmad Hijazi Mohd Suhairi Md Suhaimin Muh. Abdur Rohman Muh. Abdur Rohman Noel Segura Meraz Nurdin Bahtiar Patricia Anthony Prajanto Wahyu Adi Rahmat Gernowo Retno Kusumaningrum Rini Nuraini Rizka Putri Nawangsari Satriawan Rasyid Purnama Soni Adiyono Sukmawati Nur Endah Sutikno Sutikno Wahyul Amien Syafei Wirawan, Panji Wisnu Yesaya Rudolf Susanto Widyanto