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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) Jurnal Matematika UNAND International Journal of Advances in Data and Information Systems Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal EduHealth Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Hilirisasi Technology kepada Masyarakat (SITECHMAS) Jurnal Masyarakat Madani Indonesia eProceedings of Engineering Journal of Technology and Food Processing (JTFP) El-Mujtama: Jurnal Pengabdian Masyarakat Jurnal Kedokteran Meditek Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Folia Medica Indonesiana
Adanti Wido Paramadini
Universitas Telkom
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Veterinary Other

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Optimasi Carboxymethyl Cellulose Dan Hyaluronic Acid Sebagai Hydrogel Untuk Aplikasi Wound Dressing Pada Luka Bakar Uzlifatul Arridla, Putri; Wido Paramadini, Adanti; Afifah Zen, Nur
eProceedings of Engineering Vol. 12 No. 4 (2025): Agustus 2025
Publisher : eProceedings of Engineering

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Abstract

Luka bakar merupakan cedera kulit yang disebabkan oleh paparan panas atau bahan kimia menjadi tantangan serius dalam kesehatan masyarakat secara global. Berdasarkan data WHO, prevalensi lukabakar mencapai lebih dari 11 juta kasus yang memerlukan penanganan medis setiap tahun. Salah satu pendekatan untuk mempercepat proses penyembuhan luka adalah dengan menjaga kelembapan menggunakan wound dressing. Hydrogel sebagai wound dressing menjadi salah satu pilihanyang menarik karena kemampuannya mempertahankan kadar air yang tinggi yaitu 70–90%, sehingga menciptakan lingkungan lembap yang ideal untuk penyembuhan luka. Penelitian ini bertujuan untuk mengembangkan amorphous hydrogel berbasis carboxymethyl cellulose (CMC) dan hyaluronic acid (HA), yang diformulasikan untuk menyesuaikan luka dengan bentuk tidak teratur. CMC dipilih karena sifat hidrofiliknya yang mampu meningkatkan stabilitas mekanik hydrogel, sedangkan HA berperan dalam mendukung regenerasi jaringan, menjaga hidrasi, dan memberikan efek antimikroba. Pembuatanhydrogel dilakukan dengan mencampurkan HA 2,5% dan CMC 5% dengan variasi rasio 1:1, 1:3, 1:5 (v/v) dan HA 2,5% sebagai kontrol. Karakterisasi dilakukan melalui uji viskositas, afinitas cairan, stabilitas, dan daya sebar. Hasil penelitian menunjukkan bahwa formulasi hydrogel dengan rasio HA:CMC 1:1 memberikan performa terbaik, dengan viskositas sebesar 81.120 cps, daya serap cairan mencapai34,56%, serta stabilitas dan daya sebar yang mendukung aplikasi topikal. Dengan demikian, formulasi ini berpotensi sebagai wound dressing efektif untuk mendukung penyembuhan luka bakar secara optimal.Kata Kunci : amorphous hydrogel, carboxymethyl cellulose (CMC), hyaluronic acid, luka bakar, wound dressing
Pengaruh Konsentrasi Hidroksiapatit Terhadap Scaffold Dengan Metode Spons Replikasi Untuk Defek Tulang Juan Timotius Prasetya, Yosafat; Wido Paramadini, Adanti; Hikmah, Irmayatul
eProceedings of Engineering Vol. 12 No. 4 (2025): Agustus 2025
Publisher : eProceedings of Engineering

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Abstract

Defek tulang merupakan kondisi kehilangan struktur tulang yang memerlukan pendekatan rekayasa jaringan, salah satunya melalui penggunaan scaffold. Hidroksiapatit (HA) digunakan karena sifat biokompatibel dan osteokonduktifnya. Penelitian ini mengevaluasi pengaruh variasi konsentrasi HA (10%, 40%, dan 70%) terhadap sifat scaffold yang dibuat dengan metode spons replikasi. Scaffold dikarakterisasi melalui uji XRD untuk identifikasi struktur kristal, uji dimensi untuk kestabilanbentuk, uji degradasi dalam larutan PBS selama 7 hari, serta uji SEM-EDX untuk analisis morfologi dan komposisi kimia. Hasil menunjukkan bahwa konsentrasi HA 40% dan 70% berhasil membentuk fase kristalin HA, dengan struktur pori yang saling terhubung dan tingkat degradasi yang sesuai untuk aplikasi regenerasi tulang. Konsentrasi 70% menunjukkan porositas yang lebih baik dan kestabilan struktur lebih tinggi. Dengan demikian, scaffold HA 70% berpotensi lebih optimal untuk aplikasi pemulihan defek tulang. Kata kunci : Defek tulang, hidroksiapatit, metode sponsreplikasi, regenerasi
Pengaruh Variasi Konsentrasi Pva Pada Membran Berbasis Kitosan Untuk Aplikasi Stomatitis Aftosa Patch Menggunakan Metode Solvent Casting Afrianto, Arman; Wido Paramadini, Adanti; Permatasari, Indah
eProceedings of Engineering Vol. 12 No. 4 (2025): Agustus 2025
Publisher : eProceedings of Engineering

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Abstract

Stomatitis aftosa adalah peradangan rongga mulutyang ditandai dengan luka sariawan, menimbulkan rasa nyeridan ketidaknyamanan. Penelitian ini bertujuanmengembangkan patch berbasis kitosan dan polyvinyl alcohol(PVA) sebagai terapi lokal, menggunakan metode solvent castingdengan variasi konsentrasi PVA (2,5% dan 7,5%). Patchdikeringkan dengan dua metode, yaitu suhu ruang dan oven.Evaluasi karakteristik patch dilakukan melalui uji FTIR,mekanik, degradasi, swelling, dan SEM. Hasil FTIRmenunjukkan adanya interaksi kuat antara kitosan dan PVAmelalui ikatan hidrogen, ditandai oleh pergeseran dan pelebaranpuncak -OH dan -NH₂, serta terbentuknya struktur komposityang homogen. Uji mekanik menunjukkan peningkatankohesivitas (hingga 0,38), elastisitas (hingga 1,90 mm), danadhesi (hingga 59,85 mJ) seiring bertambahnya konsentrasiPVA, terutama pada sampel dengan rasio 1:3 yang dikeringkandi suhu ruang. Uji degradasi menunjukkan membran Oven 1:3dan Ruang 1:3 mengalami penurunan berat masing-masingsebesar 4% dan 6,25%, menandakan sifat biodegradabel yangsesuai untuk durasi aplikasi di rongga mulut. Uji swellingmemperlihatkan kemampuan penyerapan air yang sangattinggi, melebihi 1000% dalam waktu 60 menit, namunberpotensi menyebabkan disintegrasi dini pada patch jika tidakdikendalikan. Hasil SEM menunjukkan struktur permukaanyang berpori namun belum sepenuhnya homogen,mengindikasikan perlunya modifikasi formulasi, sepertipenambahan plasticizer, untuk meningkatkan kestabilan. Secarakeseluruhan, kombinasi kitosan dan PVA, terutama pada rasio1:3 dengan pengeringan suhu ruang, menunjukkankarakteristik fisikokimia dan mekanik yang paling optimaluntuk aplikasi patch stomatitis aftosa.Kata kunci— kitosan, PVA, patch, stomatitis aftosa, solventcasting.
EXPERT SYSTEM WITH DEMPSTER-SHAFER METHOD FOR EARLY IDENTIFICATION OF DISEASES DUE TO COMPLICATIONS SYSTEMIC INFLAMMATORY RESPONSE SYNDROME Wido Paramadini, Adanti; Dasril Aldo; Yoka Fathoni, M.; Yohani Setiya Rafika Nur; Dading Qolbu Adi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2021

Abstract

Systemic Inflammatory Response Syndrome (SIRS) is a generalized inflammatory condition, triggered by various factors such as infection or trauma, which can lead to serious complications if not treated quickly. This condition is characterized by symptoms such as fever or hypothermia, tachycardia, tachypnea, and changes in white blood cell count. Complications that can arise from SIRS include Acute Respiratory Distress Syndrome (ARDS), which results in fluid in the alveoli and requires mechanical ventilation; acute encephalopathy, which leads to brain dysfunction; Asidosis Metabolik, indicating liver damage; hemolysis, which results in the breakdown of red blood cells; and Deep Vein Thrombosis (DVT), which is at risk of causing pulmonary embolism. To overcome this diagnostic challenge, this study implements the Dempster-Shafer method in an expert system, where it allows the aggregation and combination of various sources of evidence to produce degrees of belief and degrees of plausibility for each diagnostic hypothesis. By accounting for uncertainties and contradictions in the data, the system improves diagnostic accuracy through dynamically weighting and updating beliefs based on available evidence. This process allows early and accurate identification of SIRS complications, supporting appropriate medical intervention. System evaluation showed diagnostic accuracy of 93%, confirming the potential of expert systems in supporting rapid and precise clinical decision-making in managing SIRS complications.
Performance Comparison of LSTM Models with Various Optimizers and Activation Functions for Garlic Bulb Price Prediction Using Deep Learning Aldo, Dasril; Paramadini, Adanti Wido; Amrustian, Muhammad Afrizal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4412

Abstract

Accurate commodity price forecasting is crucial for market stability and decision-making. This study evaluates the performance of the Long Short-Term Memory (LSTM) model using various activation functions and optimization algorithms for predicting garlic bulb prices. Historical price data was collected from panelharga.badanpangan.go.id and preprocessed through normalization and dataset splitting into training, validation, and test sets. The model was trained for 200 epochs using activation functions ReLU, Sigmoid, and Tanh, combined with optimization algorithms Adam, RMSprop, SGD, Adagrad, Adadelta, Nadam, and AdamW. Experimental results indicate that ReLU + Adam achieves the best performance with Final Epoch Loss of 0.001789, RMSE of 0.701632, MAPE of 0.009593, and R² of 0.909794, followed by Sigmoid + Nadam and Tanh + Adam, which also yielded high accuracy. These findings reinforce prior research, highlighting Adam and its momentum-based variants as effective optimizers for LSTM training. This study provides insights into selecting optimal activation functions and optimizers for commodity price forecasting. Future work may explore hybrid models and external factors, such as global market trends, to enhance predictive accuracy in time series data analysis.
In Vitro Characterization of Poly-Glycolyc Lactic-Co Acid (PLGA) –Collagen Based on Red Snapper Fish Scales (Lutjanus Sp.) Coating Chitosan as Duramater Artificial Candidate Jabbar, Hajria; Widiyanti, Prihartini; Paramadini, Adanti Wido; Putri, Dina Kartika; Isfandiary, Andini
Folia Medica Indonesiana Vol. 56, No. 3
Publisher : Folia Medica Indonesiana

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Abstract

Head trauma was the third cause of deaths that have a high rank that can make serious head injury for 25.5%-54.9%. This study has been conducted by making a replacement layer of the brain (dura) to overcome the impact of dural defect by utilizing waste fish scales red snapper (Lutjanus sp.). Synthesis brain membranes lining processed by casting method with each various concentrations of chitosan coating of 1%, 1.5%, and 2% then dried using vacuum dry. The samples then were characterized by tensile test, FTIR, SEM and MTT Assay. FTIR test results showed that red snipperscales can produce collagen powder at amide A group with stretching of –NH functional group, amide B group has stretching of CH2 assymetry, amide I area, amide II and amide III area which show –NH bonding. Tensile test results showed that the combination between PLGA-Collagen Chitosan Coating 2% produced the highest tensile strength is 4.8 MPa which meet the standards of human duramater strength. MTT Assay results showed that the dural membrane produced no toxic seen from living cells reached 98.32%. Poly - Glycolyc Lactic - Co Acid (PLGA) - collagen coating chitosan based on red snapper fish scales (Lutjanus sp.) composites has potency as duramater artificial candidate due to the chemistry, biological and physical characteristics.
Intelligent Decision Support System Based on Deep Learning with the Whale Optimization Algorithm for Oral Cancer Aldo, Dasril; Paramadini, Adanti Wido
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1521

Abstract

To build an accurate and reliable clinical decision support system, this study seeks to create a classification system using deep learning as a better approach in the analysis of oral cancer histopathological images. The dataset used consisted of 10,002 images, of which the two more balanced classes were normal oral and oral squamous cell carcinoma. Some pre-trained deep learning architectures are taken as baseline models and then optimized using the Whale Optimization Algorithm to obtain the best hyperparameter configuration. Performance evaluation was carried out on test data using accuracy, precision, recall, F1-score, confusion matrix, and operational efficiency metrics as well as evaluation of a trust-based decision support system with the same mechanism as the reject option system. The models are better optimized and all models show improved performance. From the results of the experiments, the model that was best optimized with an F1-score and an accuracy of 98.73%, and also showed the best performance, was the EfficientNet B3 model. This is accompanied by a stable training process and adequate generalization. Therefore, the model shows results with adequate performance in the coverage range of 0.60 - 0.90 and still provides a reasonable inference time for use in the clinic. These results show that this model has high potential to be integrated with clinical decision support systems. Therefore, this model can be used as a diagnostic tool in clinics that is more accurate and ensures consistency in each clinical practice and can also build a better diagnostic decision support system.
Pemberdayaan Sekolah Luar Biasa (SLB) Banyumas melalui Pelatihan Teknologi Asistif Brailltek untuk Pembelajaran Mandiri Sevia Indah Purnama; Adanti Wido Paramadini; Irmayatul Hikmah; Dodi Zulherman; Afin Muhammad Nurtsani; Rizki Amalia Pratiwi; Shinta Romadhona
El-Mujtama: Jurnal Pengabdian Masyarakat  Vol. 6 No. 3 (2026): El-Mujtama: Jurnal Pengabdian Masyarakat 
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/elmujtama.v6i3.12403

Abstract

Visually impaired students in Banyumas face significant challenges in accessing inclusive education due to the scarcity of adaptive learning media. Most students rely heavily on conventional Braille and constant teacher assistance, which hinders their independent learning capabilities. This community service aims to empower teachers and students at SLB Kuncup Mas Banyumas through the socialization and training of BraillTek, a bilingual RFID-based assistive device designed to support independent literacy. The method employed was a participatory approach involving 30 participants, consisting of teachers and students. The activities were conducted on December 9, 2025, encompassing socialization of disability rights, direct demonstration of the device, and hands-on training where students practiced using BraillTek while teachers were introduced to AI-based educational tools. The results indicated high program effectiveness, with evaluation data showing that 100% of participants agreed the material was highly relevant to their needs. Students demonstrated the ability to operate the device independently after a single session, effectively learning vocabulary without constant supervision. The program successfully introduced a viable solution for inclusive education, significantly reducing student dependence on teachers and improving teacher competency in utilizing assistive technology.
Decision Support System for Gastrointestinal Cancer Detection Using Deep Learning Aldo, Dasril; Paramadini, Adanti Wido
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.115717

Abstract

Gastrointestinal diseases are health problems that require the help of medical image analysis to improve accuracy and consistency in clinical decision-making. The main challenges with multilabel classification are visual complexity and morphological similarity. The aim of this study was to develop and evaluate an in-depth learning approach to build the first gastrointestinal tract image-based Clinical Decision Support System (CDSS). This dataset is publicly available and consists of 14 classes of gastrointestinal conditions with a total of 8,750 images, including 7,000 training images and 1,750 test images with a balanced distribution of classes. Four pre-trained convolutional neural network architectures were compared, namely MobileNetV2, MobileNetV3-Small, EfficientNet-B0, and ResNet50. The evaluation metrics used were accuracy, precision, recall, F1-score, confusion matrix, and case study inference. The experimental results showed that ResNet50 outperformed the others with 88.97% accuracy, 89.13% accuracy, 88.97% recall, and 88.94% F1-score, with multiple class analyses. Single-case inference testing on six randomly selected test images obtained a confidence value between 90-99%. The selected model is integrated into the mobile CDSS app to provide a level of confidence along with the predicted outcome. This method will likely allow for fundamental image-based evaluation to be more consistent and accountable in supporting clinical decision-making.
Edukasi Pencegahan Diabetes Dini pada Anak melalui Multimedia Interaktif TOMATOSMART KIDS Berbasis Pangan Lokal Adanti Wido Paramadini; Ajeng Dyah Kurniawati; Yohani Setiya Rafika Nur; Dasril Aldo; M. Hanif Al Faiz; Ichya Ulumiddiin; Muhammad Nafal Fiqrian
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 2 (2026): Mei
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/cgwdj470

Abstract

Peningkatan risiko diabetes melitus pada usia anak menjadi permasalahan kesehatan yang perlu mendapatkan perhatian serius, terutama akibat pola konsumsi tinggi gula dan rendahnya literasi kesehatan sejak dini. Kegiatan pengabdian masyarakat ini dilaksanakan di [nama sekolah/lokasi kegiatan] dengan melibatkan 60 peserta anak usia sekolah dasar. Kegiatan ini bertujuan untuk meningkatkan pengetahuan dan kesadaran anak dalam pencegahan diabetes dini melalui multimedia interaktif TOMATOSMART KIDS berbasis pangan lokal. Metode yang digunakan adalah pendekatan community-based dengan strategi edukasi berbasis multimedia interaktif dan learning by playing. Kegiatan dilaksanakan melalui tahapan persiapan, pelaksanaan edukasi, dan evaluasi. Evaluasi dilakukan menggunakan pre-test dan post-test serta observasi keterlibatan peserta. Hasil kegiatan menunjukkan adanya peningkatan pengetahuan peserta, dengan rata-rata skor pre-test sebesar 43,2% meningkat menjadi 90,8% pada post-test. Selain itu, peserta menunjukkan keterlibatan aktif dan sikap positif terhadap penerapan pola makan sehat. Penggunaan multimedia interaktif terbukti efektif dalam meningkatkan pemahaman peserta karena mampu menyajikan materi secara menarik, interaktif, dan sesuai dengan karakteristik anak. Dengan demikian, program TOMATOSMART KIDS berpotensi menjadi media edukasi kesehatan yang inovatif dan aplikatif dalam upaya pencegahan diabetes sejak usia dini berbasis pemanfaatan pangan lokal.
Co-Authors Afifah Zen, Nur Afin Muhammad Nurtsani Afrianto, Arman Ajeng Dyah Kurniawati Alfin Hikmaturokhman Awaludin, Muhammad Tata Dading Qolbu Adi Dasril Aldo Dasril Aldo Dermawan, Muhammad Saldi Dian Maharani Dodi Zulherman Faiz, M. Hanif Al Faizah Faizah Faizah Fathan, Faizal Burhani Ulil Fau, Andrew Firmansyah, Muhammad Raafi'u Fitranaya Arlian Fitriawan, Taufiq Gusnita Linda Haibah, Wulan Nawwar Hidayat, Afifah Naurah Ichya Ulumiddiin Ida Saroya, Nikita Ihsan Maulana Ilwan Syafrinal Indah Permatasari Irmayatul Hikmah Irmayatul Hikmah Isfandiary, Andini Jabbar, Hajria Juan Timotius Prasetya, Yosafat Lina Fatimah Lishobrina M Yoka Fathoni M. Hanif Al Faiz Mahazam Afrad Marsaly, Silvia Van Maulana, Ihsan Miftahul Ilmi Muadin, Dika Alim Muhammad Afrizal Amrustian Muhammad Nafal Fiqrian Muhammad Tata Awaludin Muzakiyah, Lukvinda Asfi Nurul Latifasari Nyimas Ananda Putri Mulyono Pamuji, Yanuar Ikhsan Pratama, Rizal Wahyu Prihartini Widiyanti Putri Anjani, Isabella Putri, Dina Kartika Rizki Amalia Pratiwi Sa'adah, Aminatus Santoso, Dimas Sapta Eka Putra Sevia Indah Purnama Sevia Indah Purnama Shinta Romadhona Silvia Van Marsally Silvia Van Marsally Suci Rahma Sari, Deli Sulaeman, Gilang Sumarsini, Erin Suprapto, Amelia Rut Uzlifatul Arridla, Putri Wijaya, Rheynaldi Yasin, Feri Yohani Setiya Rafika Nur Yohani Setiya Rafika Nur Yoka Fathoni, M.