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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Manajemen Informatika Jurnal Algoritma Eksergi: Chemical Engineering Journal JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology Sebatik Jurnal ULTIMATICS KOMPUTIKA - Jurnal Sistem Komputer METIK JURNAL Building of Informatics, Technology and Science Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) JoMMiT : Jurnal Multi Media dan IT Jurnal Algoritma Journal of Computing Theories and Applications NERO (Networking Engineering Research Operation) Journal of Multiscale Materials Informatics Jurnal Inovasi Pendidikan dan Pembelajaran
Akrom, Muhamad
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics Public Health Social Sciences

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Tree Tensor Network Quantum-Classical Hybrid Neural Architecture for Efficient Data Classification Hidayat, Novianto Nur; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12949

Abstract

We introduce the Tree Tensor Network-enhanced Quantum-Classical Neural Network (TTN-QNet), a hybrid architecture that leverages the hierarchical structure of Tree Tensor Networks for efficient parameter representation and Variational Quantum Circuits (VQC) for expressive modeling. Unlike Tensor Ring Networks, TTNs reduce parameter redundancy through a tree-based topology, enabling scalable and interpretable computation. The proposed TTN-QNet is evaluated on the Iris, MNIST, and CIFAR-10 datasets, achieving classification accuracies of 93.2%, 85.24%, and 81.67%, respectively, on binary classification tasks. TTN-QNet demonstrates rapid convergence and robustness against barren plateaus, offering a promising direction for deep quantum learning.
Evaluating Gate-Based Quantum Machine Learning Models on Quantum Chemistry Datasets Prabowo, Wahyu Aji Eko; Akrom, Muhamad
Journal of Multiscale Materials Informatics Vol. 2 No. 1 (2025): April
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jimat.v2i1.12950

Abstract

This study evaluates gate-based quantum machine learning (QML) models, including the Variational Quantum Classifier (VQC) and Quantum k-Nearest Neighbors (QkNN), on the QM9 quantum chemistry dataset for binary classification of molecular electronic properties. Using IBM Qiskit, both models were tested on simulators and real quantum hardware. Classical models (LightGBM, SVM, MLP) served as benchmarks. Results show classical models outperform quantum ones, with LightGBM achieving the highest AUC-ROC (0.901). However, VQC on simulators achieved a competitive AUC of 0.781, and real hardware still yielded performance above that of chance. Despite hardware constraints, quantum models demonstrated learning capability. The findings support hybrid quantum-classical systems as a promising near-term approach while quantum hardware continues to evolve
Klasifikasi Otomatis Korosi Menggunakan Convolutional Neural Network dan Transfer Learning dengan Model MobileNetV2 Rizky Pratama, Muhammad Hafiz; Akrom, Muhamad; Santosa, Akbar Priyo; Rosyid, Muhammad Reesa; Mawaddah, Lubna
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2182

Abstract

Corrosion is a major problem that causes significant economic losses in various industries, including transportation, energy, and manufacturing. Early detection of corrosion is essential to reduce its negative impact. This research aims to develop an automatic corrosion classification system based on Convolutional Neural Networks (CNN) with a transfer learning approach. Two models were evaluated, namely a simple CNN architecture and the pre-trained MobileNetV2. The dataset consists of corrosion and non-corrosion images divided into training, validation, and testing data. Data augmentation techniques are applied to increase the variety and number of samples in the training process. The experimental results show that MobileNetV2 achieves a testing accuracy of 95%, which is higher than that of a simple CNN that only reaches 82%. In addition, MobileNetV2 showed better performance in identifying both classes (corrosion and non-corrosion). Despite indications of overfitting due to dataset limitations, the transfer learning approach significantly improved the classification performance. This system has the potential to be applied in real-time industrial applications to reduce economic losses due to corrosion. Further research is recommended to improve the generalization of the model by using a larger dataset and applying more robust regularization techniques.
Deteksi Struktur Material Perovskit ABO3 Berbasis Machine Learning Rahman, Irfan Fauzia; Al Azies, Harun; Akrom, Muhamad
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1036

Abstract

This study proposes a machine learning-based classification approach to identify perovskite and non-perovskite structures in ABO? compounds. Perovskites have garnered significant attention as a source of functional materials, including solar cells and catalysts. Yet, discovering new materials remains a considerable challenge in terms of efficiency and exploration speed. This research addresses this gap by offering a data-driven method that automatically classifies compound structures based on crystallographic and chemical descriptors. The dataset comprises various structural and chemical features, which are analyzed using descriptive statistics, boxplot visualization, and multivariate correlation to understand the data distribution and inter-feature relationships. Four machine learning algorithms, LightGBM, XGBoost, CatBoost, and K-Nearest Neighbors (KNN), were tested and evaluated based on accuracy, precision, recall, and F1 score. Results show that LightGBM achieved the best performance with 97% accuracy, a 98% F1 score, and a confusion matrix indicating minimal classification errors. Feature importance analysis identified the tolerance factor (t), the B to O atomic radii ratio, and the AO and BO bond lengths as the most influential features. These findings highlight that tree-based boosting models effectively capture complex structural patterns, and this approach can accelerate the discovery of new materials.
Development and Implementation of a Corrosion Inhibitor Chatbot Using Bidirectional Long Short-Term Memory Ardyansyah, Nibras Bahy; Putra, Dzaki Asari Surya; Putranto, Nicholaus Verdhy; Trisnapradika, Gustina Alfa; Akrom, Muhamad
IJNMT (International Journal of New Media Technology) Vol 12 No 1 (2025): Vol 12 No 1 (2025): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v12i1.3752

Abstract

This research delves into the intricate phenomenon of corrosion, a process entailing material degradation through chemical reactions with the environment, causing consequential losses across diverse sectors. In response, corrosion inhibitors are a proactive measure to counteract this deleterious impact. Despite their paramount significance, public awareness regarding corrosion and inhibitors remains limited, necessitating intensified educational efforts. The primary focus of this study is developing a Chatbot system designed to disseminate information on corrosion, inhibitors, and related topics. Employing the Machine Learning Life Cycle model, a deep learning approach, specifically the Bidirectional Long Short-Term Memory (BLSTM) architecture, is utilized to construct an optimized Chatbot model. Post-training evaluation of the BLSTM model reveals noteworthy performance metrics, including a remarkable 100% accuracy rate and a substantial 92% validation accuracy over 100 epochs. Training and validation losses are reported as 0.2292 and 0.9342, respectively. In conclusion, the BLSTM algorithm is an effective tool for training and enhancing Chatbot models, ensuring commendable corrosion awareness and inhibition performance.
Peningkatan Hasil Belajar Matematika Menggunakan Media Manipulatif Pada Materi Pecahan Kelas V Hidayat, Ari; Desrina, Desrina; Akrom, Muhamad
Jurnal Inovasi Pendidikan Dan Pembelajaran Vol. 1 No. 1 (2025): Januari
Publisher : Arfah BHMS Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63980/eduvasi.v1i1.18

Abstract

Pada pembelajaran matematika, materi pecahan yang dilakukan di dalam Kelas VA, SD Negeri 003 Sekupang, sebagian besar ditemukan anak didik tidak memahami materi pelajaran. Hal tersebut diperkuat dengan rata-rata hasil nilai belajar anak didik dalam kegiatan prasiklus dengan jumlah anak didik 45, dan presentase ketuntasannya 33%. Salah satu sebab masalah tersebut adalah guru tidak menggunakan media pada proses pembelajaran. Penelitian ini dilakukan untuk meningkatkan hasil belajar anak didik. Subjek yang digunakan dalam penelitian adalah anak didik Kelas VA, SD Negeri 003 Sekupang, Tahun Pelajaran 2024/2025 Semester Ganjil, yang terdiri dari 28 jumlah anak didik pria dan 17 jumlah anak didik wanita. Jenis Penelitian yang dilakukan adalah Penelitian Tindakan Kelas (PTK) dengan menggunakan teknik analisis data kuantitatif. Sedangkan, teknik pengumpulan datanya melalui observasi, wawancara, dan tes penilaian tertulis. Berdasarkan hasil penelitian ini didapati hasil bahwa benar, media manipulatif dapat meningkatkan hasil belajar anak didik. Hal ini diperkuat dengan adanya peningkatan yang terjadi pada hasil belajar anak didik. Rata-rata hasil belajar dalam kegiatan siklus ke-1 dengan presentase ketuntasannya 66%. Serta, rata-rata belajar anak didik dalam kegiatan siklus ke-2 dengan presentase ketuntasannya 95%.
Peningkatan Hasil Belajar Matematika Materi Berat Benda Melalui Metode Role Playing pada Peserta Didik Kelas IV SDN 1 Sukorejo Ditha Sabrani, Amalia; Akrom, Muhamad
Jurnal Inovasi Pendidikan Dan Pembelajaran Vol. 1 No. 1 (2025): Januari
Publisher : Arfah BHMS Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63980/eduvasi.v1i1.86

Abstract

Pembelajaran matematika pada materi berat benda memerlukan peningkatan, dan salah satu cara efektif yang dapat diimplementasikan yaitu menggunakan metode role playing. Penelitian ini bertujuan untuk meningkatkan hasil belajar matematika pada materi berat benda bagi peserta didik kelas IV di SDN 1 Sukorejo. Penelitian ini merupakan penelitian tindakan kelas yang melibatkan 22 peserta didik sebagai subjek penelitian. Data dikumpulkan melalui tes tertulis dan observasi langsung selama proses pembelajaran. Hasil penelitian menunjukkan peningkatan hasil belajar yang signifikan. Pada kondisi awal, tingkat ketuntasan belajar hanya mencapai 40% pada prasiklus. Setelah menerapkan metode role playing pada siklus I, tingkat ketuntasan belajar meningkat menjadi 60%. Pada siklus II, hasil belajar peserta didik meningkat lebih jauh hingga mencapai 80%. Berdasarkan temuan ini, dapat disimpulkan bahwa metode role playing efektif dalam meningkatkan hasil belajar matematika pada materi berat benda bagi peserta didik kelas IV di SDN 1 Sukorejo. Hasil ini menunjukkan bahwa metode role playing tidak hanya menarik, tetapi juga dapat memperkuat pemahaman peserta didik terhadap materi yang disampaikan.
Optimasi model machine learning untuk prediksi inhibitor korosi berbasis augmentasi dataset senyawa n-heterocyclic menggunakan KDE Gumelar, Rizky Syah; Akrom, Muhamad; Trisnapradika, Gustina Alfa
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v%vi%i.27945

Abstract

This study aims to optimize a machine learning model to predict the corrosion inhibitor effectiveness of N-Heterocyclic compounds.  The main challenge in this modelling is the limited dataset due to the high cost and time required to collect experimental data. To overcome this problem, this research utilizes Kernel Density Estimation (KDE) as a data augmentation technique, generating virtual samples that improve dataset diversity and model predictive performance. The developed dataset includes 11 relevant chemical features such as HOMO, LUMO, and Gap Energy. Linear (MLR, Ridge, Lasso, and ElasticNet) and non-linear (KNR, Random Forest, Gradient Boosting, Adaboost, XGBoost) machine learning models were evaluated based on Root Mean Squared Error (RMSE) and coefficient of determination (R²). The results show that data augmentation using KDE improves prediction accuracy and stability, especially in non-linear models like Random Forest and XGBoost. The application of KDE proved effective in improving the performance of predictive models. It can be recommended as an augmentation method in similar studies that require additional data to improve prediction accuracy.Keywords: Machine Learning, Kernel Density Estimator (KDE), Corrosion Inhibitor, Dataset
Implementation of Jarimatika Method to Improve Multiplication Calculation Ability in Grade III Elementary School Students Akrom, Muhamad; Zihab, Zihab; Jalaluddin, Jalaluddin; Imam Muslim, Rachmat
Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Vol. 5 No. 4 (2024): November
Publisher : Mataram University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/goescienceed.v5i4.444

Abstract

This study aims to describe the use of the jarimatika method to improve multiplication calculation skills in grade III students at SD Negeri 2 Surabaya Utara in the 2023/2024 Academic Year. This research approach is a classroom action research with four stages, namely planning, implementation, observation, and reflection. Data collection techniques in this study used observation, tests and documentation. The data obtained from the research results were then analyzed using data analysis consisting of three stages, namely (1) data reduction, (2) data presentation, (3) drawing conclusions. Based on the initial conditions, the first data obtained were only 11 students (60.4%) whose scores were able to reach the KKTP while 14 students (39.6%) whose scores were below the KKTP. After conducting the research, the results obtained showed that in cycle I there were 19 students (72.48) who were able to achieve the KKTP score and 6 (27.52%) students whose scores were below the KKTP. Meanwhile, in cycle II, there were 2 students (19.28) who were below the KKTP and 23 (80.72%) students who were able to achieve the KKTP score. This shows that learning activities using the jarimatika method can improve student learning outcomes in multiplication material.
Pengabdian Kepada Masyakarat Melalui Program Pembuatan Papan Petunjuk Jalan di Desa Kerumut Wahyu Astuti, Ristina; Akrom, Muhamad; Sanusi, Muhammad; Widia Wati, Via; Husnawati Amini, Titik; Sahrul Kadri, Agus; Umam, Saripul
Jurnal Pengabdian Masyarakat Sains Indonesia (Indonesian Journal Of Science Community Services) Vol. 5 No. 2 (2023)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmsi.v5i2.258

Abstract

Real Work Lectures (KKN) are a concrete manifestation of students' duties to fulfill life skills with the guidance of lecturers. One of the aims of the KKN program carried out in Kerumut Village is to create street name boards which are an important means of providing information about the location and names of streets in an area. There are no street signs yet, which makes it difficult for outsiders to find locations in the area. KKN STKIP and STEI Hamzar East Lombok students have planned a solution to overcome this problem by making directional signs, especially in Kerumut Village. The method used is direct partner solution, namely making road signs which include 8 signboards, with the size of each board being 50 cm x 30 cm. After completing the plaque making, it was continued with painting which was then continued with the installation of the board which was carried out by students and assisted by the local community. Installation of signboards includes signs for village officials' houses, directional signs to important places as well as signs for RT and RW area boundaries in Kerumut Village. Activities carried out in 4 hamlets, namely Toron hamlet, Gubuk Daya hamlet, Benteng hamlet and Dasan Lendang hamlet, were carried out well with the support and assistance of the village residents. There were no significant obstacles in the process of preparation, implementation and evaluation of activities, it's just that the process took a long time. Making and installing road signs or plaques is a form of participation, coordination and active involvement of students, lecturers and local village residents.
Co-Authors Achmad Wahid Kurniawan Adiprasetya, Vicenzo Frendyatha Aldiansah, Ilham Pratama Alfa Trisnapradika, Gustina Amri, Sahrul Ardyansyah, Nibras Bahy Ariyanto, Noval Ayu Pertiwi Budi, Setyo De Rosal Ignatius Moses Setiadi Desrina Desrina Diah Rahayu Ningtias, Diah Rahayu Dian Arif Rachman Ditha Sabrani, Amalia Gumelar, Rizky Syah habiburrahman, Lalu Hariati, Endang Harun Al Azies Herawati, Wise Herowati, Wise Hidayat, Ari Hidayat, Novianto Hidayat, Novianto Nur Hilmy, Nur Amalina Rahmaputri HOTIMAH, NURUL Huryati, Huryati Husnawati Amini, Titik Ignasius, Darnell Ikhsanudin, Nur Imam Muslim, Rachmat Islahudin Jalaluddin Jalaluddin Jannah, Rauhun Kurniawan, Azril Lingga Dewi, Adhe Mawaddah, Lubna Miyati, Hijiul Moch Anjas Aprihartha Muhamad Fadil Muhammad Naufal Muhammad Sanusi Mulyana, Yudha Musafir, Musafir Noor Ageng Setiyanto, Noor Ageng Nur Safitri, Aprilyani Prabowo, Wahyu Aji Eko Pratama, Fikri Luqman Putra, Dzaki Asari Surya Putranto, Nicholaus Verdhy Rahman, Irfan Fauzia Rana, Bastion Jader Rizky Pratama, Muhammad Hafiz Rosyid, Muhammad Reesa Safitri, Aprilyani Nur Sahrul Kadri, Agus Santosa, Akbar Priyo setiawan, nabila putri Sheilla Rully Anggita Sudibyo, Usman Sumarjono, Cornellius Adryan Putra Supriadi Rustad T. Sutojo Trinapradika, Gustina Alfa Trisnapradika, Gustina Alfa Umam, Saripul Wahyu Astuti, Ristina Widia Wati, Via Yani, Rindi Zihab, Zihab Zulfiani, Nila