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All Journal International Journal of Electrical and Computer Engineering JSI: Jurnal Sistem Informasi (E-Journal) Telematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal ICT : Information Communication & Technology Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Journal of Applied Data Sciences Journal of Community Development Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Wahana Jurnal Penelitian Ilmu Pendidikan Indonesia Wanastra: Jurnal Bahasa dan Sastra Pengabdian: Jurnal Abdimas
Linda Perdana Wanti
Politeknik Negeri Cilacap
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Pelatihan SIPAKPRIH untuk Deteksi Dini Preeklamsia sebagai Dukungan Peningkatan Kinerja IBI Kabupaten Cilacap Linda Perdana Wanti; Nur Wachid Adi Prasetya; Lina Puspitasari; Laura Sari; Annisa Romadloni
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 3 (2023): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v7i3.11586

Abstract

IBI (Indonesian Midwives Association) Cilacap Regency is a forum for the association of midwife medical personnel in the Cilacap Regency. The performance of midwives can be continuously improved through training that supports all health service activities in the community. One of them is training in the use of information systems to detect the presence of preeclampsia in pregnant women (SIPAKPRIH) from the first to the third trimester by selecting the causative factors experienced by pregnant women. Midwives can take advantage of the expert system to support the performance of midwives in terms of health services for the community, especially pregnant women and the babies/fetus they contain. The solution proposed through this PkM activity is to improve the performance of midwives, especially midwives in Cilacap Regency in supporting health service activities to the community that are useful for monitoring the health of mothers and babies during pregnancy. The output target of this PkM activity is to increase the skills and knowledge of midwives for monitoring the health of pregnant women who are detected with preeclampsia through optimizing SIPAKPRIH.
Expert System for Diagnosing Inflammatory Bowel Disease Using Certainty Factor and Forward Chaining Methods Linda Perdana Wanti; Nur Wachid Adi Prasetya; Oman Somantri
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2096

Abstract

Identification of inflammatory bowel disease quickly and accurately is motivated by the large number of patients who come with pain in the abdomen and receive minimal treatment because they are considered to be just ordinary abdominal pain. This study aims to identify inflammatory bowel disease which is still considered by some people as a common stomach ache quickly, and precisely and to recommend therapy that can be done as an initial treatment before getting medical action by medical personnel. The method used in this expert system research is a combination of forward chaining and certainty factors. The forward chaining method traces the disease forward starting from a set of facts adjusted to a hypothesis that leads to conclusions, while the certainty factor method is used to confirm a hypothesis by measuring the amount of trust in concluding the process of detecting inflammatory bowel disease. The results of this study are a conclusion from the process of identifying inflammatory bowel disease which begins with selecting the symptoms experienced by the patient so that the diagnosis results appear using forward chaining and certainty factor in the form of a percentage along with therapy that can be given to the patient to reduce pain in the abdomen. A comparison of the diagnosis results using the system and diagnosis by experts, in this case, specialist doctors, shows an accuracy rate of 82,18%, which means that the expert system diagnosis results can be accounted for and follow the expert diagnosis.
Fuzzy expert system design for detecting stunting Linda Perdana Wanti; Oman Somantri; Nur Wachid Adi Prasetya; Lina Puspitasari
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp556-564

Abstract

Stunting is a chronic nutritional problem that occurs in toddler due to lack of nutritional intake which results in impaired growth toddler. Usually, toddler who experience stunting are characterized by not increasing weight over a long period of time. Application utilization health which makes it easier for users to access information, one of which can be used to identify toddler who are stunted by selecting symptoms. The symptoms experienced by toddlers go through a system known as the system expert. In this research an expert system will be developed that is capable of early detection developmental disorders in toddlers using the Mamdani fuzzy method. The results obtained from this research are an expert system design for early detection of stunting using the Mamdani fuzzy method. The Mamdani fuzzy method was implemented to group the criteria for toddlers who fall into the stunting category or not from the initial data which is still gray because they are still unsure whether to categorize the toddler as having stunting or not. The detection accuracy rate using the Mamdani fuzzy method is 80.87% compared to expert diagnosis.
Comparison of The Dempster Shafer Method and Bayes' Theorem in The Detection of Inflammatory Bowel Disease Linda Perdana Wanti; Nur Wachid Adi Prasetya; Oman Somantri
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.1797

Abstract

This study discusses the comparison of the Dempster-Shafer method and Bayes' theorem in the process of early detection of inflammatory bowel disease. Inflammatory bowel disease, better known as intestinal inflammation, attacks the digestive tract in the form of irritation, chronic inflammation, and injuries to the digestive tract. Early signs of inflammatory bowel disease include excess abdominal pain, blood when passing stools, acute diarrhea, weight loss, and fatigue. The Dempster-Shafer method is a method that produces an accurate diagnosis of uncertainty caused by adding or reducing information about the symptoms of a disease. Meanwhile, Bayes' theorem explains the probability of an event based on the factors that may be related to the event. This study aims to measure the accuracy of disease detection using the Dempster-Shafer method compared to the probability of occurrence of the disease using Bayes' theorem. The results of calculating the level of accuracy show that the Bayes Theorem method is better at predicting inflammatory bowel disease with a probability of occurrence of disease in the tested data of 75.9%.
Fuzzy Expert System for Decission Support to Diagnosis Leukemia Linda Perdana Wanti; Nur Wachid Adi Prasetya; Zahrun Nafisa; Rahmat Mulyadi; Muhammad Ramadani
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2349

Abstract

Leukemia is a cancer of the blood and bone marrow. In leukemia, the bone marrow produces too many abnormal white blood cells. These abnormal cells cannot fight infections well and can displace healthy blood cells, which can cause anemia and bleeding. In this study, a fuzzy method will be implemented to diagnose leukemia and the results will later be compared with expert diagnoses. Fuzzy logic was chosen because it allows for degrees of truth between 0 (completely false) and 1 (completely true) and it is suitable for situations where human expertise relies on experience and judgment rather than fixed rules. Fuzzy systems can analyze large amounts of data quickly, thereby accelerating the diagnosis and decision-making process, especially when used in medical decision support systems. This study produced a leukemia diagnosis accuracy of 88.83% when compared with the results of expert diagnoses using the same symptom and sample data.
Development of a Hybrid CNN–SVM-Based Acute Lymphoblastic Leukemia Detection System on Hematology Image Data Linda Perdana Wanti; Annisa Romadloni; Kukuh Muhammad; Abdul Rohman Supriyono; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.3002

Abstract

Acute Lymphoblastic Leukemia (ALL) is among the most common pediatric blood cancers and progresses rapidly, necessitating early and accurate detection. Manual diagnosis via microscopic analysis of blood samples is time-consuming and highly dependent on specialist expertise. This study proposes a hybrid model that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM) to automatically detect ALL from blood-cell images. The CNN performs deep feature extraction from images, while the SVM serves as the classifier to determine ALL status. The dataset comprises microscopic images labeled as ALL or normal and is processed through preprocessing steps such as augmentation and normalization. The adopted CNN produces optimized feature representations. Experimental results show that the hybrid CNN–SVM model with an RBF kernel achieves the best performance, with an accuracy of 96.4%, precision of 95.8%, recall of 96.1%, and an F1-score of 96.0%, surpassing pure CNN-based baselines. Training converged at the 41st epoch, with a training accuracy of 97.2%, validation accuracy of 95.9%, training loss of 0.09, and validation loss of 0.11, indicating stable learning without overfitting. The model’s ROC curve lies well above the chance diagonal, with an Area Under the Curve (AUC) of 0.914, means there is a 91.4% chance the model assigns a higher score to a truly positive (leukemia) image than to a negative (normal) image.These findings suggest that the CNN–SVM hybrid approach enhances leukemia detection performance compared with conventional CNN-only methods and holds promise as a fast, accurate, and efficient image-based decision-support tool for early leukemia diagnosis in digital hematology.
Pelatihan Pengolahan Limbah Kayu Mahoni (Swietenia Mahagoni) Pada BUMDes Banjarwaru Radhi Ariawan; Nur Akhlis Sarihidaya Laksana; Unggul Satria Jati; Roy Aries Permana Tarigan; Bayu Aji Girawan; Linda Perdana Wanti; Nur Wachid Adi Prasetya; Ganjar Ndaru Ikhtiagung
Journal of Community Development Vol. 5 No. 2 (2024): December
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i2.258

Abstract

Banjarwaru village is one of the villages in Cilacap district with the potential for wooden broom handle craft commodities. The processing of mahogany wood (Swietenia mahagoni) into broom handles produces wood waste that has not been utilized optimally. The accumulation of wood waste is caused by a lack of knowledge among the Banjarwaru village woodworking community regarding the potential dangers, benefits, and economic value of wood waste. Therefore, training in wood waste processing into value-added products such as particleboard is needed. The Community service team together with BUMDes Banjarwaru carried out mentoring activities as a solution method for utilizing wood waste. The mentoring activities carried out consist of educating the negative effects of wood waste on the environment, educating the potential and benefits of wood waste in another processed forms, and training in wood waste processing into particleboard. These series of activities succeeded in increasing the woodworking community understanding of wood waste by 81.6%. The success rate of mentoring reached 91,67% shown by 11 out of 12 mentoring participants understood the potential danger, benefits, and other processed forms of wood waste.
Penguatan Kapasitas Digital UMKM Desa Menganti melalui Pelatihan Pemanfaatan Aplikasi Online untuk Pemasaran Produk LINDA PERDANA WANTI; Rahmawan Bagus Trianto; Abdul Rohman Supriyono; Annisa Romadloni; Muhammad Abdul Muin; Antonius Agung Hartono; Hety Dwi Hastuti
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 8 No. 1 (2026): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/abdimaspolibatam.v8i1.12145

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Menganti Village, Cilacap Regency, play a vital role in driving the local economy, yet they face limitations in utilizing digital technology for product marketing. Most MSMEs still rely on conventional marketing methods, resulting in relatively limited market reach and competitiveness. This community service program aims to improve the digital capacity of MSMEs through training in the use of online applications as a means of marketing their products. The program implementation method includes needs identification, outreach, hands-on training, and mentoring in the use of digital marketing applications, such as social media and marketplace platforms. The training focused on digital account management, creating simple promotional content, and implementing easy-to-implement online marketing strategies tailored to the characteristics of village MSME products. The results of the program indicate an increase in the knowledge, skills, and confidence of MSMEs in utilizing online applications to market their products more widely. Participants are now able to manage digital media independently and understand the importance of digital marketing as part of business development. This program is expected to encourage the independence and sustainability of MSMEs in Menganti Village in facing the challenges of the digital economy and continuously increase the competitiveness of local products.
Peningkatan Kapasitas UMKM Kabupaten Cilacap Melalui Kecerdasan Buatan dan Keamanan Transaksi Digital Bella Adinda Putri; Linda Perdana Wanti; Satriawan Desmana; Krisna Nuresa Qodri; Ratih Ratih; Abdul Rohman Supriyono; Muhammad Nur Faiz; Oman Somantri; Ratih Hafsarah Maharrani
Wahana Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 1 (2026): Edisi Juni
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/wahana.v5i1.1778

Abstract

UMKM di era digital menghadapi tantangan karena kurangnya pemahaman tentang literasi digital dan kesadaran akan ancaman siber. Kegiatan ini bertujuan untuk meningkatkan pemahaman pelaku UMKM–yang didukung oleh BAZNAS Kabupaten Cilacap–dalam memanfaatkan teknologi AI dan aplikasi keamanan digital. Metode pelaksanaannya menggunakan pelatihan praktik langsung, diskusi interaktif, dan pendampingan langsung melalui dua tahap: (1) pengenalan konsep AI untuk keamanan transaksi digital; dan (2) pelatihan tentang aplikasi pendukung keamanan digital (GetContact, Kredibel, dan VirusTotal). Kegiatan ini melibatkan 100 pelaku UMKM dari berbagai sektor usaha. Evaluasi yang dilakukan melalui pre-test dan post-test menggunakan 10 pertanyaan terkait materi pelatihan menunjukkan peningkatan yang signifikan dengan rata-rata skor peserta meningkat dari 78,09 (pre-test) menjadi 97,50 (post-test), yang menggambarkan peningkatan sebesar 19,41 poin atau 28,98%. Hal ini menunjukkan bahwa pelatihan tersebut efektif meningkatkan literasi keamanan digital dan memberdayakan pelaku UMKM untuk melindungi bisnis mereka dari ancaman kejahatan siber dan penipuan online.
Optimized Skill Mastery Prediction for Adaptive Test Decision-Making Linda Perdana Wanti; Rujianto Eko Saputro; Fandy Setyo Hutomo; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3171

Abstract

Adaptive testing systems require accurate and timely estimation of students' skill mastery levels to support decision-making in question selection and assessment flow determination. However, uncertainty in students' knowledge levels and variations in learning behavior often limit the performance of conventional predictive models. This study proposes an optimized predictive modeling framework for skill mastery to enhance decision-making quality in adaptive testing. The proposed framework integrates a machine learning-based skill mastery prediction model with an optimization mechanism to improve model accuracy and stability, while accommodating the sequential nature and uncertainty of student responses. Learning interaction data is used to dynamically model the development of skill mastery levels, which are then utilized as decision-support input in the adaptive testing system. The proposed predictive skill mastery model shows strong and consistent performance with an AUC value of 0.822, Average Precision of 0.868, accuracy of 0.757, and a precision balance of 0.834, recall of 0.788, and F1-score of 0.810, supported by well-calibrated probabilities and the ability to respond adaptively to student learning dynamics, making it suitable for use to support decision-making in adaptive test decision-making systems. The results of this study confirm the potential of integrating predictive analytics and optimization techniques in developing intelligent adaptive assessment systems.
Co-Authors Abdul Rohman Supriyono Akmal Fauzan Ananta Alif Iftitah Andesita Prihantara Andriansyah Zakaria Anggita Dwi Fatimah Anne Johanna Annisa Romadloni Annisa Romadloni Antonius Agung Hartono Bayu Aji Girawan Bella Adinda Putri Dwi Novia Prasetyanti Eka Tripustikasari Erna Alimudin Fadillah Fadillah Fandy Setyo Hutomo Fandy Setyo Utomo Ganjar Ndaru Ikhtiagung Hety Dwi Hastuti Hijriah Fajar Muhammad Insan Ilham Aditya Pangestu Isa Bahroni, Isa Krisna Nuresa Qodri Kukuh Muhammad Kurniawan Yogi Laksono Laura Sari Laura Sari Lina Puspitasari Muhammad Abdul Muin Muhammad Nur Faiz Muhammad Ramadani Nur Akhlis Sarihidaya Laksana Nur Wachid Adi Prasetya Nur Wahyu Rahadi Oman Somantri Qisthi Alhazmi Hidayaturrohman Radhi Ariawan Rahmat Mulyadi Rahmawan Bagus Trianto Ratih Hafsarah Maharrani Ratih Ratih Renanda Sinta Nurul Kholia Riyadi Purwanto Rostika Listyaningrum Roy Aries Permana Tarigan Rujianto Eko Saputro Satriawan Desmana Sofyan Setiajid Syahrur Romadhon Titin Kartiyani Unggul Satria Jati Zahrun Nafisa