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All Journal Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Electronics, Electromedical Engineering, and Medical Informatics Abdi Teknoyasa Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia
Umi Salamah
Department Of Informatics, Faculty Of Information Technology And Data Science, University Of Sebelas Maret, Surakarta, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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PEMBELAJARAN DAN PENDAMPINGAN PEMANFAATAN SPSS UNTUK MENINGKATKAN KOMPETENSI OLAH DATA STATISTIK BAGI GURU DI SMA NEGERI 1 KEMUSU BOYOLALI Wiharto; Esti Suryani; Umi Salamah; Nurcahya PTP; Sigit Setyawan
Abdi Teknoyasa Volume 1, No.2, Desember 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (627.524 KB) | DOI: 10.23917/abditeknoyasa.v1i2.196

Abstract

Penulisan karya tulis ilmiah bagi guru merupakan salah satu Persyaratan kenaikan pangkat yang harus dilakukan. Hal ini menyebabkan banyak guru yang kemudian berlomba-lomba dalam membuat karya ilmiah. Akibatnya adalah guru kesulitan dalam rangka naik jabatan fungsionalnya karena beberapa karya ilmiah kurang memenuhi standar, dimana data yang disajikan tidak dapat merepresentasikan keseluruhan isi tulisan. Kemampuan pengolahan dan penyajian data penelitian merupakan faktor penting dalam menciptakan karya ilmiah yang baik. Guru di beberapa SMA di Kabupaten Boyolali antara lain adalah SMA Negeri 1 Kemusu merupakan salah satu potret yang mengalami masalah tersebut. Hal inilah yang melatarbelakangi pengabdian menganai pemanfaatan SPPS untuk meningkatkan kompetensi guru dalam mengolah data statistic, khisusnya di SMA Negeri 1 Kemusu Boyolali. SPSS (Statistical Package for the Social Sciences) merupakan salah satu aplikasi perangkat lunak komputer yang digunakan untuk analisis statistik. Tujuan dari aplikasi ini adalah mempermudah tugas guru dalam mengolah data untuk artikel dan publikasi ilmiah sedangkan tujuan dari kegiatan pengabdian kepada masyarakat ini adalah untuk meningkatkan kemampuan guru -guru di SMA Negeri 1 Kemusu dalam melakukan pengolahan data penelitian sebagai data pendukung karya ilmiah. Kegiatan pengabdian ini dilakukan dalam empat tahapan. Tahap pertama adalah identifikasi kemampuan pengolahan data statistik dan identifikasi penentuan materi utama praktik yang perlu ditonjolkan. Tahap kedua adalah tahap pelaksanaan workshop yang diisi dengan turorial penggunaan SPSS hingga pemanfaatan SPSS dalam menyelesaikan masalah statistik. Tahap ketiga adalah tahap simulasi penggunaan SPSS yang dilakukan langsung oleh seluruh peserta workshop. Tahap keempat adalah evaluasi dan tindak lanjut. Pendampingan dan pelatihan SPSS bagi para guru khususnya guru SMU 1 Kemusu Boyolali secara umum dapat meningkatkan pengetahuan dan kekampuan peserta dalam melakukan olah data statistik. Sebelumnya dilakukan pelatihan telah ada peserta yang mengetahui adanya aplikasi olah data statistik sebanyak 30% dan setelah diadakan pelatihan SPSS 100% dari peserta mengetahui dan dapat meningkatkan kemampuan peserta dalam melakukan analisis data statistik dengan menggunakan SPSS, ini berarti secara umum ada 70% peningkatan kemampuan dalam olah data statistiK dengan menggunakan SPSS, meskipun masih banyak kendala yang dihadapi oleh peserta, terutama masih kurang terbiasa dan kurang latihan 85%.
Desimination of technology for increasing the security of community-based citizens of Gawanan village, Colomadu using i-siskamling Umi Salamah; Wiharto Wiharto; Ristu Saptono
Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Vol. 1 No. 10 (2021)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jppmi.v1i10.48

Abstract

The residents of Gawanan Village, RT 02 and 04 RW 12 Colomadu, Karanganyar have almost the same characteristics: most of the residents work during the day and they are migrants. It gives security problems during the day, especially in an empty house and a long holiday. The vast area and the number of access roads, as well as proximity to the center of the crowd such as malls, factories, and warehouses, cause very inefficient when using security services during the day. Therefore, i-Siskamling which IP Camera Outdoor using internet access can be one solution to overcome these security problems. IP cameras are installed in strategic places. As a result, nine CCTV cameras with two controls (NVR) were installed with monitors at the two Partner locations. The results are very encouraging as seen from the absence of reports of theft in the two regions and the monitoring of several disturbing events in the community. In addition, community support was very evident with providing the electricity for the camera and access points and two additional CCTV cameras and accessories for the system.
Classification of Early Stages of Lung Cancer based on First and Second Order Statistical Variations using Decision Tree Method Soeparmi Soeparmi; Umi Salamah; Arnita Ayu Ningrum; Mohtar Yunianto
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.48026

Abstract

Purpose: This research aims to produce the best performance in identifying early-stage lung cancer class through CT-Scan image analysis using the decision tree classification method and to determine the results of the best classification performance from the variations carried out.Methods: Five steps in the CT-Scan image classification process for early-stage lung cancer class based on tumor density measurements. First, image data preparation where the image data used was 280 CT-Scan images with a pixel size of 607 x 607 and PNG format taken from the LIDC-IDRI database at https://www.cancerimagingarchive.net/ with a total of 1010 CT-Scan data scans. Second, the grayscaling stage converts the RGB image to a grayscale. Third, combining a high pass filter and Gaussian smoothing filter method is used to remove salt pepper noise and to smooth the image. Fourth, the feature extraction stage uses first and second-order statistics with 22 features used. The fifth is the classification stage using a decision tree, which is then validated using the k-fold method with k=10 so that all image data can be tested thoroughly.Result: The accuracy rate at the training stage was 90.51%, and at the testing stage was 89.99%. Stage I lung cancer detection program through CT-Scan imagery was successfully created with the highest PSNR value proven to optimize the accuracy level, precision, and recall in the testing phase results of 89.99%, 91.24%, and 89.64%.Novelty: Based on previous research searches, no one had used machine learning to classify early-stage lung cancer. Punithavathy et al. (2015) and Meliala (2021) stated that early detection of lung cancer can increase survival by 60%-70%. This research will produce a new method for determining early-stage lung cancer. 
Classification of Ultrasound Images Using ResNet-50 with a Convolutional Block Attention Module (CBAM) Bagus Tegar Zahir Afif; Wiharto Wiharto; Umi Salamah
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1406

Abstract

Liver fibrosis staging is a crucial component in the clinical management of chronic liver disease because it directly affects prognosis, therapeutic decision-making, and long-term patient monitoring. Ultrasound imaging is widely used as a noninvasive diagnostic modality due to its safety, low cost, and broad accessibility. Nevertheless, ultrasound-based fibrosis assessment remains challenging because liver parenchymal echotexture often exhibits low contrast, speckle noise, and subtle inter-stage variations, particularly among adjacent METAVIR stages. These characteristics frequently limit the effectiveness of conventional convolutional neural networks, which tend to emphasize dominant global patterns while suppressing weak but clinically meaningful texture cues. This study presents a task-oriented integration of a Convolutional Block Attention Module into a ResNet-50 backbone to enhance feature discrimination for five-stage liver fibrosis classification using heterogeneous B-mode ultrasound images. Rather than introducing a new attention mechanism, the contribution lies in the systematic insertion of CBAM after residual outputs across multiple network stages, enabling repeated channel and spatial recalibration from low-level texture descriptors to higher-level semantic representations. To further improve robustness and reduce prediction variance, a stratified 5-fold training strategy is combined with logit-level ensemble inference, where logits from independently trained fold models are averaged prior to Softmax normalization. Experiments were conducted on a publicly available dataset comprising 6,323 ultrasound images acquired from two tertiary hospitals using multiple ultrasound systems, with fibrosis stages labeled from F0 to F4 according to histopathology-based METAVIR scoring. The proposed framework achieves a test accuracy of 98.34%and consistently high precision, recall, and F1 scores across all fibrosis stages, with the most pronounced improvement observed for intermediate stages. Statistical analysis based on paired fold-wise comparisons confirms that the performance gain over the baseline ResNet 50 model is statistically significant. These results demonstrate that combining lightweight attention-based feature refinement with logit ensemble inference effectively addresses the inherent challenges of ultrasound-based liver fibrosis staging and provides a reliable noninvasive decision support framework with strong potential for clinical application and future multicenter validation.
Mental Health Detection Expert System Model Based on DASS-42 Using Fuzzy Inference System Eko Ginanjar Basuki Rahmat; Wiharto Wiharto; Umi Salamah
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1443

Abstract

Mental health disorders such as depression, anxiety, and stress frequently co-occur and exhibit overlapping symptoms, making accurate diagnosis challenging due to the subjective nature of psychological assessments. Conventional use of the Depression Anxiety Stress Scales (DASS-42) relies on rigid score aggregation, while many machine learning approaches fail to adequately represent uncertainty and expert reasoning. This study aims to develop an expert system for mental health detection by integrating fuzzy logic with expert knowledge derived from the DASS-42 instrument. The main contribution of this research is a hybrid knowledge-based framework that combines decision tree–based rule extraction with psychological expert validation, ensuring both interpretability and clinical relevance. The proposed method employs a Fuzzy Inference System (FIS) using triangular and trapezoidal membership functions to model symptom intensity as linguistic variables, followed by rule generation using the CART decision tree algorithm and expert refinement. System performance is evaluated using Cohen’s Kappa coefficient, including standard error and 95% confidence intervals, to measure inter-rater reliability between the expert system, the DASS instrument, and two human experts. The results indicate that the expert system achieves almost perfect agreement in identifying dominant psychological conditions, with an average Kappa value of 0.918. For severity-level classification, strong agreement is observed for depression (Kappa = 0.842) and stress (Kappa = 0.811), while anxiety severity shows moderate-to-substantial agreement (Kappa = 0.648), reflecting inherent variability in expert interpretation. In conclusion, the proposed FIS-based expert system effectively captures expert diagnostic reasoning and outperforms decision tree–only models, demonstrating strong potential as an interpretable and reliable mental health screening tool.
Fuzzy-IOWA-Based Group Decision Support System (GDSS) for Interpreting DASS Scores with Dynamic Expert Weighting Wiharto; Eka P. Meravigliosi; Umi Salamah; Esti Suryani; Vihi Atina; Pradityo Utomo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7340

Abstract

This research develops a Group Decision Support System (GDSS) to address subjectivity and ambiguity in the interpretation of Depression Anxiety Stress Scales (DASS-21) scores, particularly in cases of symptom overlap across dimensions. The system introduces a structured weighting mechanism in which the influence of each expert is determined based on objective criteria, including clinical experience, education level, and academic contributions. The methodology applies Simple Additive Weighting (SAW) to quantify the relative importance of five clinical experts, resulting in Expert 2 (27.66%) and Expert 1 (27.36%) having the highest influence within the group. These weights are then incorporated into a Fuzzy-Induced Ordered Weighted Averaging (Fuzzy-IOWA) framework to aggregate expert judgments into a unified consensus model. The results indicate that the proposed approach is able to produce a consistent interpretation structure, with a tendency toward the Anxiety dimension in cases of overlapping symptoms. By integrating expert consensus with patient self-report scores, the system generates a structured interpretation profile of DASS-21 responses. The proposed GDSS provides a systematic and transparent aggregation framework that reflects expert reasoning. However, it is intended as a methodological support tool rather than a substitute for clinical diagnosis. The novelty of this work lies in the integration of SAW-based objective expert capability quantification with Fuzzy-IOWA aggregation and QGDD-based consensus characterization, forming a transparent and mathematically accountable GDSS pipeline that has not been previously applied in the context of DASS-21 interpretation.
Co-Authors Arnita Ayu Ningrum Atina, Vihi Bagus Tegar Zahir Afif Eka P. Meravigliosi Eko Ginanjar Basuki Rahmat Esti Suryani Esti Suryani Mohtar Yunianto Nurcahya PTP Pradityo Utomo Ristu Saptono Sigit Setyawan Soeparmi Soeparmi Wiharto Wiharto Wiharto Wiharto Wiharto Wiharto Wiharto Wiharto