Lab-IS/filkom-research-papers huggingface dataset:
Research on Structural Flexibility and Acceptance Model (SFAM) Reconstruction based on Disruption Innovation in the Social Humanities and Education Sector
Authors: ADJI ACHMAD RINALDO FERNANDES SOLIMUN, Intan Rahmawati, Lailil Muflikhah, Farid Ubaidillah, Alifya Al Rohimi
Journal: WSEAS Transactions on Mathematics
Year: 2021
Topics: Social Sciences; Education
Abstract: The research objectives are as follows:(1) Developing a flexible structural model of the relationship between variables.(2) Develop a structural model that is robust with the assumptions of normality and homoscedasticity.(3) Obtain estimator properties from the flexible and robust SFAM structural model.(4) Obtaining hypothesis testing of each relationship constructed from a flexible and robust SFAM structural model. This research is integrated with a flexible and robust model approach based on nonparametric smoothing spline (RNSS) regression analysis which can capture the form of relationships that depend on empirical data, and the robustness of the model based on the distribution assumption and the assumption of homoscedasticity error variance. There are at least three transformation methods, namely SRS, MSI, and RASCH, which will be used in the development of the Structural Flexibility and Acceptance Model (SFAM). The results obtained from the research progress report are obtaining the development of a flexible structural model of the form of the relationship between variables, obtaining the development of a robust structural model of the assumptions of normality and homoscedasticity, obtaining the estimator properties of the flexible and robust SFAM structural model, and obtaining hypothesis testing. of each relationship constructed from a flexible and robust SFAM structural model. The originality of the theory in this study is very visible in the discovery of a new model, namely SFAM which can accommodate several things, which are the weaknesses of several existing analysis tools such as reciprocal and recursive models, more …
Color moment and gray level co-occurrence matrix in classification of soil organic matter for patchouli plantation
Authors: Candra Dewi, Mudawikawi Akbar Grahadhuita, Lailil Muflikhah
Journal: Indonesian Journal of Electrical Engineering and Computer Science
Year: 2020
Topics: Machine Learning; Agriculture
Abstract: Patchouli is one of the essential plants that have the most potential and widely cultivated in Indonesia. Patchouli is greedily absorbing soil nutrients and organic matter. Therefore, the selection of soil with high organic matter will maximize the patchouli’s productivity. This paper aims to facilitate soil’s organic matter identification by classifying soil image based on the combination of color and texture features. The color feature extraction was done using the Color Moments method and the texture feature was done using Gray Level Co-occurrence Matrix (GLCM) method. The selection of features was performed to obtain the best combination of color and texture features. The selected features then was used as input of classification by using Modified K-Nearest Neighbor (MKNN). The samples of soil that used as data were taken from several districts in Blitar, East Java province. The testing result of this research showed the highest accuracy of 93, 33% by using 180 training data, and also particular color and texture feature combination.
Application of Artificial Intelligence for Maternal and Child Disorders in Indonesia: A Review
Authors: Diva Kurnianingtyas, Lailil Muflikhah
Year: 2024
Topics: Artificial Intelligence; Medical Research
Abstract: The development of Artificial Intelligence (AI) technology is used to minimize the risk of maternal disorders during pregnancy. Maternal health needs to be monitored so as not to cause problems during the baby's birth. The purpose of this study is to provide a literature review from 2017 to 2023. The method used is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study is a basis for researchers to help solve maternal health problems using AI. This technology is proven to detect and predict problems during pregnancy, thereby reducing maternal and infant mortality and preventing abnormalities in the development process. In addition, AI can help improve medical personnel's performance by minimizing human error. This study also presents trends in AI problems and methods used. However, the rapid development of AI methods has not provided novelty in solving maternal …
Comparative Analysis of Summarization Methods for Skin Care Product Reviews: A Study on BERT, BART, and T5 Models
Authors: Nisrina Arintia Maghfiroh, Fitra Abdurrachman Bachtiar, Lailil Muflikhah
Year: 2023
Topics: Natural Language Processing; Text Mining
Abstract: The growth of the skin care industry is driving consumers to look for product review information. The skincare product currently widely used by women and men is facial wash products. On the Female Daily website, the most frequently purchased facial wash products are Cetaphil products. With so many purchases of this product, potential buyers want to try the product by reading the reviews first. However, the large number of reviews is a barrier to accessing relevant information. Therefore, effectively summarizing reviews can solve this problem by presenting key information more concisely and digestibly. This study aims to evaluate the effectiveness of three different summary methods, namely BERT, BART, and T5, in summarizing skin care product reviews. The stages in these three models are explained in detail. Evaluation results using the ROUGE Score method with the Cetaphil facial wash product review dataset …
Drug Resistant Prediction Based on Plasmodium Falciparum DNA-Barcoding using Bidirectional Long Short Term Memory Method
Authors: Lailil Muflikhah, Nashi Widodo, Novanto Yudistira, Achmad Ridok
Journal: International Journal of Advanced Computer Science and Applications
Year: 2023
Topics: Machine Learning; Genomics
Abstract: Malaria disease mostly affects children and causes death every year. Multiple factors of the disease due to failure in treatment, including anti-malaria drug resistance. The resistance is caused by a decrease in the efficacy of the drug against Plasmodium parasites. Therefore, we proposed a computational approach using deep learning methods to predict anti-malarial drug resistance based on genetic variants of the Plasmodium falciparum through DNA barcoding. The DNA Barcode, organism identification from Plasmodium, is employed as data set for predicting the anti-malaria drug resistance. As a univariate amino acid sequence, it is transformed to numerical value data for building classifier model. It is constructed into a classifier model for prediction using Bidirectional Long Term-Short Memory (Bi-LSTM). This algorithm is extended from LSTM by two directions. In the first stage, the sequence is encoded into numerical data as input data for the method using sigmoid activation loss function. Then binary cross entropy is addressed to define the class, resistance or sensitivity. The final stage is applied by tuning hyper-parameter using Adaptive Moment Estimation optimizer to get the best performance. The experimental results show that Bi-LSTM as the proposed method achieves high performance for resistance prediction including precision, recall, and f1-score.
Mutation-Induced Changes in the Stability, B-Cell Epitope, and Antigenicity of the Sars-Cov-2 Variant Spike Protein: A Comparative Computational Stud
Authors: Nira Meirita Wijayanti, Muhammad Hermawan Widyananda, Lailil Muflikhah, Nashi Widodo
Journal: Karbala Int J Mod Sci
Year: 2023
Topics: Computational Biology; Immunology
Abstract: The spike (S) protein is a major antigenicity site that targets neutralizing antibodies and drugs. The growing number of S protein mutations has become a severe problem for developing effective vaccines. Here, we investigated four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that were the most infectious and widespread during the COVID-19 pandemic to determine the trends and patterns of mutation-induced changes in the stability, B-cell epitope, and antigenicity of the SARS-CoV-2 S protein. The data showed that the Beta and Gamma variants had three mutations on the receptor-binding domain (RBD), which is the specific site on the S protein for angiotensin-converting enzyme 2 (hACE2) binding. The Delta variant had only two mutations, whereas the Omicron variant had 15 mutations on the RBD. The results showed that the stability of the S protein varied and depended on the mutation type and that Gamma and Omicron are the most stable of the four variants analyzed. The S protein–hACE2 complexes of the Beta and Gamma variants were relatively stable after 20 ns of simulation compared with those of the Delta and Omicron variants. We predicted that the B-cell epitopes of the mutant S protein would be different from those of the wildtype. Moreover, the antigenicity of Omicron changed drastically compared with that of the other variants. Bioinformatics analysis and a molecular dynamic simulation revealed that the mutations affected the stability of the S protein. A large number of mutations do not always stabilize the S protein. Mutations in Omicron significantly altered the B-cell epitope and antigenicity, which …
Peningkatan Kinerja Yayasan Pendidikan melalui SIMAG (Sistem Informasi Manajemen Gaji)
Authors: Dian Eka Ratnawati, Rekyan Regasari MP, Lailil Muflikhah
Journal: DIMASLOKA: Jurnal Pengabdian Masyarakat Teknologi Informasi Dan Informatika
Year: 2022
Topics: Information Systems; Educational Management
Abstract: Adanya peningkatan kebutuhan sekolah swasta yang berkualitas membutuhkan pula peningkatan kinerja Yayasan Pendidikan. Salah satu aspek kinerja yang menjadi permasalahan dalam Yayasan Pendidkan adalah dalam penggajian, baik alat bantu agar perhitungan gaji dan pencetakan slip gaji menjadi lebih mudah maupun dalam penentuan formula gaji yang tepat. Kegiatan pengabdian ini bertujuan untuk meningkatkan kinerja Yayasan Pendidikan dalam pengelolaan gaji sehingga karyawan (guru dan tenaga kependidikan) mendapatkan kepuasan dan kenyamanan dalam keputusan mengenai gaji yang mempengaruhi aspek sosial kesejahteraan mereka. Terkait hal tersebut, maka target dari kegiatan ini adalah pembuatan aplikasi Sistem Informasi Manajemen Gaji (SIMAG) sekaligus pelatihan dalam pengoperasiannya. Tahapan penelitian meliputi penggalian dan analisis kebutuhan pengguna, perancangan sistem, implementasi, dan pengujian sistem. Pengujian dilakukan oleh mitra yang merupakan pengguna sistem. Jika dalam pengujian masih ada yang kurang sesuai, maka implementasi diperbaiki hingga sesuai dengan kebutuhan pengguna. Selanjutnya dilakukan pelatihan penggunaan sistem. Sebagai bentuk pelayanan akan tetap diberikan pendampingan hingga mitra benar-benar memahami penggunaan sistem. Hasil evaluasi sistem melalui kuisioner dan wawancara dengan ketua Yayasan menunjukkan bahwa Sistem Informasi Manajemen Gaji (SIMAG) dapat meningkatkan kinerja yayasan pendidikan dalam mengelola gaji guru. Kelebihan lain SIMAG adalah dinamis dan adaptif terhadap perubahan parameter …
Machine Learning dalam Bioinformatika
Authors: Lailil Muflikhah, Wayan Firdaus Mahmudy
Year: 2021
Topics: Machine Learning
Abstract: Pesatnya kemajuan teknologi informasi yang mengakibatkan mudahnya perolehan data dalam jumlah yang relatif banyak, mendorong banyaknya penelitian menggunakan pendekatan komputasi. Begitu pula dalam bidang biologi molekuler, ledakan data terhadap materi genetika digunakan untuk analisis biologi terhadap gejala alam yang ada, termasuk dalam dunia kedokteran untuk membantu diagnosis pasien terhadap gejala suatu penyakit. Hepatoma merupakan suatu penyakit kanker hati, yang salah satu penyebabnya adalah infeksi virus hepatitis B. Terjadinya penyakit ini relatif lama dalam perkembangannya dari kronis, sirosis dan fibrosis hingga terjadi hepatoma, namun sering terjadi keterlambatan dalam penanganannya. Hal ini disebabkan kurangnya gejala-gejala klinis yang muncul (silent symptoms) hingga mendorong banyaknya penelitian ke arah biologi molekuler dengan menggunakan materi genetika dari pasien untuk membangun aplikasi dengan metode machine learning. Metode ini menggunakan pendekatan komputasi berbasis data dalam membuat rule sehingga mampu mengenali suatu penyakit. Buku ini dapat digunakan sebagai referensi untuk mengembangkan suatu aplikasi diagnosis atau prognosis suatu penyakit dengan menggunakan metode machine learning. Adapun kandungan dari buku ini, dimulai dari latar belakang pentingnya pendekatan machine learning dalam bioinformatika, termasuk konsep dasar dan cakupan machine learning dan bioinformatika. Selanjutnya pembahasan terkait profiling salah satu penyakit, yakni hepatoma menggunakan data sekuen DNA maupun ekspresi gen host. Peranan …
Sistem Diagnosis Penyakit Kelamin Pada Pria Menggunakan Metode Forward Chaining Dan Dempster-Shafer
Authors: Kukuh Bhaskara, Nurul Hidayat, Lailil Muflikhah
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Medical Diagnosis; Artificial Intelligence
Abstract: Menurut World Health Organization (WHO), terdapat sebanyak kurang lebih satu juta kasus infeksi menular seksual (IMS) terjadi pada tiap harinya. Dapat diperkirakan terdapat sebanyak 357 juta kasus infeksi menular seksual baru di seluruh belahan dunia yang terjadi tiap tahunnya. Infeksi menular seksual merupakan semua infeksi yang ditularkan oleh virus, bakteri maupun jamur dari satu individu ke individu lain melalui hubungan seksual atau melalui organ genital. Bayi yang baru saja dilahirkan memiliki risiko tertular penyakit gonore karena ibunya akibat melewati organ genital yang terinfeksi bakteri N. Gonorrhoeae. Menurut WHO, pada saat ini terdapat lebih dari 30 jenis bakteri, virus dan juga parasit yang telah teridentifikasi dapat mengakibatkan infeksi menular seksual. Sebanyak 8 dari 30 jenis bakteri, virus dan parasit ini merupakan risiko terbesar dalam mengakibatkan infeksi menular seksual. Tujuan dari penelitian ini ialah mengimplementasikan metode Forward Chaining dan Dempster-Shafer dan menguji keakuratan akurasi metode ini dalam mengidentifikasi penyakit kelamin pada pria. Hasil dari penelitian yaitu berdasarkan 35 data uji, akurasi yang didapatkan sistem diagnosis ini adalah sebesar 94, 2% yang membuktikan jika sistem diagnosis ini dapat berjalan dengan sangat baik dan sesuai dengan diagnosis yang dihasilkan oleh pakar.
Implementasi Convolutional Neural Network untuk Klasifikasi Kanker Usus Besar Dengan Normalisasi Ghostweight
Authors: Galih Restu Baihaqi, Budi Darma Setiawan, Lailil Muflikhah
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2025
Topics: Deep Learning; Medical Imaging
Abstract: Kanker usus besar merupakan salah satu kanker yang dapat menyebabkan kematian. Jenis kanker ini merupakan kanker peringkat kedua terbanyak pada wanita dan peringkat ketiga terbanyak pada pria. Akhir–akhir ini, pendekatan Deep Learning (DL) banyak digunakan untuk melakukan proses–proses dalam dunia medis. Salah satu metode yang terkenal yaitu Convolutional Neural Network (CNN). CNN tentunya harus memiliki akurasi yang tinggi untuk dapat diterapkan pada kasus ini. Dataset yang digunakan adalah dataset Lung and Colon Cancer Histopathological Images yang berfokus pada kanker ususnya saja. Salah satu cara yang dapat digunakan untuk meningkatkan akurasi pada CNN adalah dengan menormalisasi bobot. Untuk ini, metode yang diusulkan adalah Ghost Weight Normalization (GWN) dengan normalisasi L1 yang terinspirasi dari GhostNet. Metode ini bekerja dengan cara melakukan pembagian bobot utuh menjadi beberapa bagian yang dinamakan GW dan kemudian dinormalisasi untuk setiap GW-nya, lalu digabung kembali menjadi bobot utuh seperti semula. Pendekatan ini terbukti dapat meningkatkan akurasi CNN dengan sangat baik, yaitu mengalami penambahan akurasi sebesar 14% yang semula CNN biasa memperoleh akurasi sebesar 0.8 menjadi 0.94, presisi 0.8 menjadi 0.94 dan F1-score 0.8 menjadi 0.94. GWN juga dapat mengungguli gaya normalisasi biasa, yaitu normalisasi pada bobot tanpa membaginya menjadi GW. Ukuran GW yang efisien adalah 4 dengan perolehan akurasi, persisi, dan f1-score masing-masing 0.94, dengan epoch 8 dan rata – rata untuk waktu proses training-nya pada …
Comparison Genetics Algorithm and Particle Swarm Optimization in Dietary Recommendations for Maternal Nutritional Fulfillment
Authors: Diva Kurnianingtyas, Nathan Daud, Indriati Indriati, Lailil Muflikhah
Journal: SITEKIN: Jurnal Sains, Teknologi dan Industri
Year: 2024
Topics: Optimization Algorithms; Nutrition
Abstract: Fulfilling maternal nutrition is an NP-hard problem. Optimization techniques are required to solve its complexity. This issue is crucial as it affects the number of stunted toddlers in Indonesia. Stunting begins in the womb due to inadequate maternal nutrition during pregnancy. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are optimization methods applied to NP-hard problems, including medicine. Their performance has not been compared in this field. This study aims to identify an alternative method for recommending daily menus based on maternal nutritional needs. There are 55 food ingredients used to fulfill five menu parts: staple food (SF), vegetables (VG), plant source food (PS), animal source food (AS), and complementary (CP). Nutritional adequacy for prenatal is determined by Total Energy Expenditure (TEE) based on basal energy, daily activity, and stress levels. Results show PSO …
Facial Palsy Detection Through Changes in Facial Muscle Functionality Using CNN Algorithm
Authors: Chindy Aulia Sari, Fitra Abdurrachman Bachtiar, Lailil Muflikhah, Aris Widayati
Year: 2023
Topics: Deep Learning; Medical Diagnosis
Abstract: Facial palsy is a common condition in the medical field that results from facial nerve disorders, leading to weakened facial motor functions. Detecting facial palsy typically involves observing the functional ability of facial muscles, a process currently performed manually by healthcare professionals relying on visual cues. However, manual detection is prone to errors, this needs for early and automated facial palsy detection. Overall, our research aims to improve the detection of facial palsy by leveraging deep learning techniques. By automating the detection process, we hope to provide a more accurate and efficient approach for early identification of this condition. In this research, we aim to develop a facial palsy detection system using the Convolutional Neural Network (CNN) algorithm. We utilize three datasets, namely CK+, JAFFE, and YouTube Facial Palsy, which are organized into normal and dysfunctional …
Optimasi Gizi pada Lanjut Usia dengan Alergi untuk Meningkatkan Kualitas Hidup menggunakan Algoritma Genetika
Authors: Adhika Ramadhan, Imam Cholissodin, Lailil Muflikhah
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2023
Topics: Optimization Algorithms; Nutrition
Abstract: Masih banyak masyarakat Di Kecamatan mojo yang masih kurang mengetahui mengenai asupan gizi yang optimal gizi yang kurang optimal dapat menyebabkan penyakit seperti osteoporosis, gangguan pencernaan (kerusakan pada lambung dan usus) dan juga dapat memicu gangguan mental contohnya seperti demensia atau dapat juga memicu depresi, selain kekurangan gizi kelebihan gizi juga dapat berdampak buruk pada kesehatan manusia lanjut usia contoh dari efek buruk kelebihan gizi adalah penyakit darah tinggi, diabetes, hingga stroke sehingga asupan gizi seharusnya ditakar dengan baik agar terhindar dari penyakit penyakit yang merugikan menurut laporan RKD (Riset Kesehatan Dasar) pada tahun 2018 populasi lansia di indonesia berada pada 16.2% dan diprediksi akan terus naik hingga seperlima dari total penduduk indonesia sehingga apabila tidak mulai menerapkan gizi yang seimbang dikhawatirkan akan terjadi masalah baru di indonesia. masalah mengenai optimasi gizi dapat diselesaikan menggunakan algoritma genetika, algoritma genetika merupakan algoritma yang dapat menghasilkan rekomendasi susunan bahan makanan yang bervariasi dan memiliki biaya konsumsi yang rendah. Untuk mencapai nilai yang maksimal maka digunakan parameter sebagai berikut nilai iterasi terjadi konvergensi awal pada iterasi ke 900 nilai populasi terbaik ada pada 450 lalu nilai Cr (Crossover) 0, 7 dan nilai Mr (Mutation rate) pada 0, 8
Implementation of Discourse Network Analysis Related to News regarding PT Pertamina's Consumer Needs, Satisfaction, and Engagement
Authors: Adji Achmad Rinaldo Fernandes, Solimun Solimun, Lailil Muflikhah
Journal: International Journal of Mathematics and Mathematical Sciences
Year: 2022
Topics: Network Analysis; Business Analytics
Abstract: The purpose of this study is to implement Discourse Network Analysis (DNA) and analyze the results in the formation of networks related to issues and actors in a news regarding consumer needs, satisfaction, and attachment to PT Pertamina. This study is the qualitative method that applies a qualitative Discourse Network Analysis (DNA) approach. Sources of DNA data come from various information in cyberspace (mass media, journals, articles, etc.) that are by the research context. Based on the search results in cyberspace, we obtained about 68 relevant news sources discussing consumer service on BBM products PT. Results of DNA taken from 68 news stories in cyberspace show that there are 45 actors from 14 different organizations who have opinions about PT Pertamina's consumers. Two actors only have a negative opinion, two actors have positive and negative opinions, and 41 actors have a positive …
Detection of hepatitis B virus-associated hepatocellular carcinoma disease using hybrid hierarchical k-means clustering and SVM algorithm
Authors: Lailil Muflikhah, Nashi Widodo, Wayan F Mahmudy, Solimun
Journal: Current Bioinformatics
Year: 2021
Topics: Machine Learning; Medical Diagnosis
Abstract: Background Hepatocellular carcinoma (HCC) is a serious disease and is the third main cause of death in the world. Hepatitis B virus infection can lead to HCC. The virus introduces genetic material into the host, damages DNA, and interferes with the activity of the apoptotic and tumor suppressors to trigger the formation of an oncogene. However, most of these cases are discovered after cancer enters stage three or four. Objective Early detection of HCC through machine learning algorithm approach using data set: DNA sequence of HBx HepB virus. Methods The research method used is the development of a Support Vector Machine classifier algorithm for carcinoma detection. The large data volume and unbalance data distribution in class can decrease the accuracy rate and sensitivity. Therefore, this paper proposed a hybrid of Hierarchical k-Means clustering and SVM algorithms to detect HCC disease using …
Deteksi Nefropati Diabetik Pada Pasien Diabetes Melitus Menggunakan Regresi Logistik
Authors: Muhammad Fihan Ashidiq, Lailil Muflikhah, Budi Darma Setiawan
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2025
Topics: Medical Diagnosis; Machine Learning
Abstract: Diabetes Melitus adalah penyakit metabolik kronis yang ditandai oleh hiperglikemia akibat gangguan produksi atau penggunaan insulin. Diabetes melitus dapat menyebabkan komplikasi serius, salah satunya Nefropati Diabetik, yang merupakan kerusakan pada ginjal akibat diabetes. Berdasarkan data International Diabetes Federation (IDF), jumlah penderita diabetes meningkat signifikan setiap tahun. Penelitian ini bertujuan mendeteksi kemungkinan terjadinya Nefropati Diabetik pada pasien Diabetes melitus menggunakan model regresi logistik. Tahap awal penelitian ini melibatkan pengolahan data (preprocessing) untuk mengatasi masalah data outlier yang berlebih dan ketidakseimbangan kelas. Metode balancing data dengan oversampling dan log transformasi digunakan untuk meningkatkan kualitas dataset. Proses pelatihan model dilakukan dengan algoritma gradient descent untuk mengoptimalkan parameter regresi logistik. Evaluasi model menunjukkan hasil yang sangat baik, dengan akurasi, presisi, recall, dan F1-score rata-rata mencapai 0, 98. Penelitian ini diharapkan dapat menjadi alat prediksi yang efektif dalam mendeteksi Nefropati Diabetik pada pasien diabetes melitus, sehingga membantu pencegahan komplikasi yang lebih serius. Implementasi model ini dapat berkontribusi pada peningkatan kualitas pelayanan kesehatan, terutama dalam penanganan kasus diabetes melitus.
Lagerstroemin from Lagerstroemia speciosa as antibreast cancer candidate targeting AURKA, EGFR and SRC protein: a comprehensive computational study
Authors: Muhammad Hermawan Widyananda, Nuraini Rosyadah, Lailil Muflikhah, Nashi Widodo, Dinia Dwijayanti, Siti Mariyah Ulfa
Year: 2024
Topics: Computational Biology; Drug Discovery
Abstract: Breast cancer is a type of cancer that has a high rate of diagnosis and mortality in the world. Lagerestroemia speciosa is a herb that has anticancer activity. This study aims to analyze the compounds in L. speciosa that most act as anticancer of the breast. The compounds in L. speciosa were selected based on drug-likeness, physicochemical properties, ability to penetrate the lipid bilayer and toxicity. The Lagerstroemin target related to Lagestroemin breast cancer was predicted using DisGeNET, SWISS Target Prediction and cBioportal. Molecular docking between Lagerstroemin and AURKA, EGFR and SRC was performed using AutoDock Vina. The interaction stability of each complex was analyzed by molecular dynamic simulation using YASARA with parameters of RMSD protein, RMSD ligand, number of hydrogen bonds and molecular dynamic binding energy. Of the 22 compounds, Quercetin, Caffeic acid, Lagerstroemin and Dypirone were predicted to have good ADME properties and can easily penetrate lipid membranes. Therefore, Quercetin, Caffeic acid and Lagerstroemin were predicted to have anti-breast cancer bioactivity. Of the 3 compounds, Lagerstroemin had the lowest toxicity. Lagerstroemin was predicted to interact with breast cancer-related proteins AURKA, EGFR and SRC. Molecular docking and dynamics showed that Lagerestroemin interacted stably at the ATP binding site of the 3 proteins, so it was very potential as an inhibitor of these 3 proteins. Therefore, Lagerstroemin was predicted to be the compound in L. speciosa with the most potential breast anticancer agent by targeting AURKA, EGFR and SRC.
High Performance for Predicting Diabetic Nephropathy Using Stacking Regression of Ensemble Learning Method.
Authors: Lailil Muflikhah, Amira Ghina Nurfansepta, Fitra Abdurrachman Bachtiar, Dian Eka Ratnawati
Journal: International Journal of Online & Biomedical Engineering
Year: 2024
Topics: Machine Learning; Ensemble Learning
Abstract: Diabetes may lead to several problems, one of the most prevalent and deadly of which is diabetic nephropathy. Therefore, the condition represents a significant threat to one's health since it has the potential to cause irreversible harm to the kidneys' ability to operate. A significant portion of the research that is being conducted now is focused on determining how accurately diabetic people may be predicted to develop kidney illness. Considering this, the research suggests a regression stacking approach for predicting albumin levels. These albumin values will serve as a reference for the incidence of diabetic nephropathy disease. They will be derived from the medical records of patients. The utilization of stacking regression from three different ensemble approaches, using Random Forest and CatBoost regressors, while the Huber algorithm is used as a meta-learner. The accuracy with which the combination of …
Analisis sentimen kebijakan masuk sekolah pukul lima pagi menggunakan algoritma Naïve Bayes
Authors: Wilhelmina Sonya Hoar, Anis Zubair, Lailil Muflikhah
Journal: Journal of Information System and Application Development
Year: 2024
Topics: Sentiment Analysis; Machine Learning
Abstract: Education is very important to build a quality young generation that can advance the nation. To improve the quality of education, the government of East Nusa Tenggara implemented a five am school entry policy. However, this policy has caused pros and cons in the community. People are becoming more active expressing their opinions through social media. Criticism of this policy is reflected in comments appearing on social media, especially on Twitter. Therefore, it's important to conduct sentiment analysis to know how many positive and negative responses to this policy. In this research, sentiment analysis was carried out on search results for tweets with the keyword “sekolah jam 5 di NTT” in the time period from February to March 2023. A total of 777 tweets were obtained with 24 positive sentiments and 753 tweets with negative sentiments. The data was then processed and analyzed using the Naïve Bayes …
Question answering system for factoid questions about COVID-19 with natural language processing approach
Authors: Indriati Indriati, Randy Cahya Wihandika, Putra Pandu Adikara, Barlian Henryranu Prasetio, Lailil Muflikhah
Journal: AIP Conference Proceedings
Year: 2024
Topics: Natural Language Processing; Artificial Intelligence
Abstract: In March 2020, Indonesia faced fast-spreading COVID-19 Disease. In this regard, self-isolation serves as a solution for patients with mild symptoms due to the government's limited isolation facilities. Self-isolation is different from COVID-19 treatment in the hospital, where patients are fully treated by health professionals. Information clarity is pivotal amid the pandemics due to a vast number of misleading information. Addressing this issue, it is important to keep selfisolation patients receiving adequate and accurate information about COVID-19. Question Answering System (QAS) with Natural Language Processing emerges as one of the solutions to provide information for users. Named Entity Recognition is one of the methods to search for the answer for factoid questions, allowing users to obtain accurate answers. A number of question types were set as keywords with 50% to 72% accuracy.
High performance of Dengue shock syndrome detection using extreme gradient boosting with ANOVA feature selection.
Authors: Lailil Muflikhah, Agustin Iskandar, Novanto Yudistira, Isbat Uzzin Nadlori, Bambang Nur Dewanto
Journal: Journal of Biotech Research
Year: 2024
Topics: Machine Learning; Feature Selection
Abstract: Dengue shock syndrome (DSS) is an infectious disease that affects millions of people every year all over the world. Early detection of DSS is essential for providing effective therapy and promoting patient recovery. In this work, we proposed a method to enhance DSS detection by combining the Extreme Gradient Boosting (XGBoost) algorithm with variance (ANOVA) feature selection analysis. We used a clinical dataset that contained important information gleaned from people with dengue virus infection. The dataset used for the research was collected from patients at Syaiful Anwar Hospital and consisted of 501 instances. Of these, 401 cases were related to DSS, while the other instances were unrelated to this specific medical condition. An analysis of variance (ANOVA) evaluated the most significant factors that distinguished persons with DSS from those with other dengue diseases. After that, the XGBoost model gave the characteristics of the selected features. For evaluation, we split the data into 80% and 20% for training and testing, respectively. The experimental result showed that the created model had a high degree of performance evaluation in detecting DSS. The highest performance achieved an accuracy of 0.839, precision of 0.875, recall of 0.92, and f1-score of 0.897. Most importantly, the model could detect DSS early on, enabling more proactive therapy and faster responses for individuals at risk of developing the condition.
Up Sampling Data in Bagging Tree Classification and Regression Decision Tree Method for Dengue Shock Syndrome Detection
Authors: Lailil Muflikhah, Agustin Iskandar, Novanto Yudistira, Bambang Nur Dewanto, Isbat Uzzin Nadhori, Lisa Khoirun Nisa
Year: 2023
Topics: Machine Learning; Data Sampling
Abstract: Dengue virus, DENV, is the cause of dengue fever and carries the risk of developing dengue hemorrhagic fever and dengue shock syndrome (DSS), which can lead to death. The high mortality rate is due to the blockage of blood circulation resulting from delayed patient management, particularly in cases of advanced-stage DSS. Intensive examinations and new treatments are only provided when the patient has been admitted to the hospital. Therefore, we proposed to develop an early detection for DSS risk based on the clinical data using ensemble method through bagging Tree-CART, namely Tree-Bag algorithm. We explored information from patient’s clinical data including temperature, vomiting, pain, as well as the levels of erythrocytes, leukocytes, creatine level, hemoglobin, and time series data on the patient's condition. In the preprocessing data stage, we applied oversampling data due to imbalanced class …
Classification Comparison of Activity Distraction Detection in Car Driving
Authors: Fitra A Bachtiar, Ferry Ardianto Rismawan, Erland Hilman Fuadi, Novanto Yudistira, Lailil Muflikhah
Year: 2022
Topics: Machine Learning; Classification
Abstract: Currently, development of safety in driving is advancing. Technology in safety in driving can be found in cars. A higher safety precautions is expected with lots of technology implemented in the car to minimize road accidents. However, most of the safety detection is built for the car and the external environment. Meanwhile, the human factor can also have determining role in safety driving. Safety driving to detect driver behavior is still not widely implemented. Therefore, a way is needed to be able to detect safe driving with the driver as the object. Furthermore, the development of an image-based safety driving system is a challenge so that the goal of driver safety can be achieved. Thus, there is a need to develop a system that could detect driver activity during driving. As an initial exploration, this study aims to compare four methods of machine learning and deep learning with the task of classifying activity distraction in …
Indonesian Stock Prices Prediction using Bidirectional Long Short-Term Memory
Authors: I Gede Angga Dinata, Novanto Yudistira, Lailil Muflikhah
Year: 2022
Topics: Deep Learning; Time Series Analysis
Abstract: This paper aims to know how well Bidirectional Long Short-Term Memory (BiLSTM) is in predicting Indonesian stock prices. First, the best hyperparameter of BiLSTM is searched through hyperparameter tuning. After finding the best hyperparameter, we train the data train containing close prices from Indonesian stock. After that, we try to find optimal days to predict by measuring the error rate using Mean Absolute Error (MAE). We also compared BiLSTM with Recurrent Neural Network (RNN) and LSTM by comparing the MAE from each method. Finally, we also tried using multivariate BiLSTM using Indonesian stock. The evaluations yield the best hyperparameters setting and how many days suitable for predicting BiLSTM performance. BiLSTM performed better than RNN and LSTM. Moreover, univariate BiLSTM performs better than multivariate BiLSTM in predicting Indonesian stock prices.
Pelatihan Budidaya Multi-Culture Farming Berbasis Teknologi Sistem Pakar serta Optimasi untuk Kemandirian Ekonomi dan Ketahanan Pangan Masyarakat Indonesia
Authors: Sutrisno Sutrisno, Imam Cholissodin, Arief Andy Soebroto, Lailil Muflikhah
Journal: JAST: Jurnal Aplikasi Sains dan Teknologi
Year: 2022
Topics: Expert Systems; Agricultural Technology
Abstract: Poverty alleviation efforts in Indonesia are still challenging due to the lack of job opportunities. Therefore, creative efforts are needed that are easy to do at home, especially during this Covid-19 pandemic. In this service activity, the creative business was introduced, which was packaged in multi-culture farming cultivation training. The community service activities were preceded by conducting a field survey on three partners, ie, the farmer groups in Sunge Geneng Village, Sekaran District, Lamongan Regency, RT 3/RW III Kauman Village Malang City, and Poncokusumo District Malang Regency. Since the survey plan was carried out during the Covid-19 Pandemic and the frequent Public Activity Restrictions (PPKM), the team could only survey the initial two partners. However, during the implementation process, the second partner was the only one to reach the implementation stage of Multi-Culture Farming with non-AI technology and introduce the use of AI Technology. Therefore, the second partner, RT 03/RW 03 Kauman Village, Klojen District, Malang City, also became the leading partner. The main results of activities were ten budikdamber tools, catfish feed, and training modules about optimizing plant fertilizer nutrition. The other results were land-use using AI Engine and educational videos that received an excellent response from the partners and non-partners being sustainable to create a kind of fostered village, especially in entrepreneurship that utilizes digital technology. ABSTRAK Upaya pengentasan kemiskinan di Indonesia masih sulit dilakukan, karena minimnya lapangan pekerjaan. Oleh karena itu dibutuhkan usaha kreatif yang …
Rekomendasi Kelayakan Peminjam menggunakan Metode Fuzzy Tsukamoto
Authors: Muh Hamim Fajar, Edy Santoso, Lailil Muflikhah
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2021
Topics: Fuzzy Logic; Decision Making
Abstract: Koperasi merupakan salah satu bidang usaha yang menawarkan simpan pinjam. Untuk menjadi anggota koperasi atau untuk melakukan simpan pinjam seseorang harus memenuhi persyaratan yang sudah ditentukan oleh koperasi. karena keterbatasan dana yang dimiliki koperasi, maka pemilihan seseorang agar mendapat bantuan dilakukan untuk mengurangi masalah pembayaran. Pada penelitian ini dilakukan perhitungan agar dapat merekomendasikan kelayakan peminjam menggunakan pendekatan metode fuzzy tsukamoto. Terdapat beberapa proses dalam perhitungan metode Fuzzy Tsukamoto untuk menyelesaikan permasalahan tersebut, ialah Fuzzyfikasi, sistem Inferensi Fuzzy Tsukamoto dan defuzzyfikasi. Pada proses fuzzyfikasi ialah merubah variabel nyata menjadi variabel fuzzy. Selanjutnya, proses sistem inferensi fuzzy tsukamoto ialah mencari nilai Î±-predikat dan z, dan terakhir defuzzyfikasi ialah merubah variabel fuzzy menjadi variabel nyata. Berdasarkan dari hasil pengujian yang dilakukan menggunakan metode fuzzy tsukamoto dari data koperasi KUDâ€ SRI LESTARIâ€ diperoleh hasil performansi yang sangat baik dengan nilai akurasi sebesar 87, 88% dan nilai F-1 Score sebesar 92, 96%.
Detection of Hepatoma based on Gene Expression using Unitary Matrix of Singular Vector Decomposition
Authors: Lailil Muflikhah, Nashi Widodo, Wayan Firdaus Mahmudy, Ninik Nihayatul Wahibah
Journal: International Journal of Advanced Computer Science and Applications
Year: 2021
Topics: Bioinformatics; Linear Algebra
Abstract: Hepatoma is a long-term disease with a high risk of mortality. However, the disease is late detected, at the fourth level stadium due to silent symptoms. The infected hepatitis B virus gene HBx is a genome virus to trigger liver disease. This virus inserts material genetic into the host and disturbs the cell cycle. The regulation of gene expression is blocked to make work abnormal, especially for repairing and degrading. A microarray is a tool to quantify the RNA gene expression in huge volumes without any information for the related potential gene. Therefore, this study is proposed a feature extraction method using a unitary singular matrix for simplifying the classification model of hepatoma detection. Principally, the feature is decomposed using a singular vector to get the k-rank value of pattern. This matrix is applied to the representative machine learning algorithm, including KNN, Naïve Bayes, C5. 0 Decision Tree, and …
Pelatihan Pelaporan Keuangan Berbasis Aplikasi di Lingkup Antar RT dalam RW Kelurahan Kauffman Kecamatan Klojen Malang
Authors: Lailil Muflikhah
Journal: JAST
Year: 2020
Topics: Financial Reporting; Application Development
Abstract: Pemanfaatan teknologi informasi dan komunikasi untuk aplikasi keuangan berbasis online telah banyak dikembangkan, diantaranya di bidang perbankan, keuangan, transportasi, jual beli, kesehatan, dan sebagainya. Demikian juga aplikasi berbasis perangkat bergerak seperti telepon seluler, komputer, laptop maupun lainnya juga sudah lazim dan banyak digunakan oleh masyarakat. Berdasarkan kondisi tersebut, maka dilaksanakan kegiatan pengabdian masyarakat antar RT dalam satu RW di wilayah Kelurahan Kauman Kecamatan Klojen Kota Malang (khususnya RT-02 dan RT-03 di RW-03) untuk menggunakan aplikasi keuangan. Aplikasi yang dikembangkan diharapkan dapat mempermudah dalam melakukan pengelolaan keuangan di tingkat RT, sehingga lebih efektif, efisien, akurat dan transparan. Tahapan kegiatan pengabdian kepada masyarakat ini diawali dengan melakukan observasi dan survei khususnya RT-02 dan RT-03 di RW-03 Kelurahan Kauman Kecamatan Klojen Kota Malang. Hasil survei ini dijadikan dasar implementasi aplikasi keuangan yang dikembangkan. Selanjutnya dilakukan sosialisasi dan pelatihan penggunaan aplikasi yang telah berhasil dikembangkan dari referensi terpilih, agar masyarakat lebih terbiasa dalam menggunakan sistem layanan keuangan tersebut. Dengan adanya aplikasi ini, diharapkan pengelolaan layanan keuangan menjadi lebih mudah dan pengguna aplikasi juga dimudahkan dengan adanya fitur-fitur yang telah disediakan. Hasil dari kegiatan ini yaitu, Sistem Pengelola Keuangan RT (SIPUT) dapat diakses dalam berbagai platform baik perangkat PC desktop maupun web, lalu …
Implementasi Fuzzy k-Nearest Neighbor (Fk-NN) untuk Klasifikasi Jenis Kanker berdasarkan Susunan Protein
Authors: Tahtri Nadia Utami, Marji Marji, Lailil Muflikhah
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Fuzzy Logic
Abstract: Penyakit kanker merupakan penyakit paling mematikan setelah penyakit jantung. Penyebab umum terjadinya kanker adalah mutasi gen yang terjadi pada protein 53 yang berfungsi untuk mengendalikan replikasi DNA sebagai pengatur fungsi sel sehingga menyebabkan kesalahan susunan protein. Susunan protein dijadikan dasar dalam mengklasifikasi jenis kanker yang kemudian dapat membantu menentukan penanganan atau terapi yang tepat. Klasifikasi jenis kanker dilakukan dengan menggunakan metode Fuzzy k-Nearest Neighbor (Fk-NN). Data yang digunakan merupakan 752 data sekuens protein dengan panjang sekuens yaitu 393. Kelas klasifikasi meliputi kelas bukan kanker, kelas kanker payudara, kelas kanker usus dan kelas kanker paru-paru. Metode Fk-NN menghitung derajat keanggotaan masing-masing kelas pada k jarak terkecil dari hasil perhitungan jarak metode k-Nearest Neighbor (k-NN). Rata-rata tingkat akurasi tertinggi yaitu 52.56% dari hasil pengujian menggunakan k-fold-validation. Nilai k optimal metode Fk-NN adalah k= 5 dengan rata-rata tingkat akurasi sebesar 54.99%. Variasi jumlah data latih yang besar yaitu 90% dari jumlah dataset menghasilkan tingkat akurasi tertinggi sebesar 55.33%.
Penerapan Algoritme Genetika untuk Optimasi Penjadwalan Jam Kerja Part-Time Studi Kasus Cafe Bingsoo Malang
Authors: Yogi Suwandy, Lailil Muflikhah, Tibyani Tibyani
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Genetic Algorithms; Optimization
Abstract: Jam kerja Part-Time atau yang sering disebut jam kerja paruh waktu adalah sekelompok pekerja yang secara bergantian menggantikan kelompok yang lain setelah waktu yang dikerjakan telah berakhir. Jam kerja part-time yang diterapkan di suatu instansi biasanya membaginya dalam 3 shift kerja, shift pagi, shift siang dan shift malam. Tidak sedikit mahasiswa yang memilih kerja Part-Time untuk kuliah sambil bekerja untuk mengisi waktu kosong dijam kuliah karena dengan bekerja mereka dapat menambah uang saku dan pengalaman mereka (Saftarina F & Hasanah L, 2013). Seorang manager atau ketua divisi yang berperan untuk mengatur jadwal kerja harus teliti dalam pembagian shift karyawan agar semua karyawan mendapatkan jumlah jam kerja yang sama. Penjadwalan merupakan suatu kegiatan untuk mencari solusi dari suatu masalah yang mana akan menghasilkan susunan jadwal yang optimal (Mawaddah & Mahmudy, 2006). Algoritme genetika merupakan metode yang telah banyak diterapkan oleh para peneliti untuk mendapatkan suatu solusi dari permasalah penjadwalan (Hayati, 2013). Algoritme Genetika yang digunakan mampu memberikan nilai akurasi sebesar 100% terhadap 9 karyawan divisi waiters pada Cafe Bingsoo Malang, Pengujian dilakukan menggunakan beberapa nilai parameter terbaik diantaranya jumlah populasi sebesar 70, banyaknya generasi sebanyak 70, crossover rate sebesar 0.6 dan mutation rate 0.5 dengan nilai fitness yang didapatkan sebesar 1.
Klasifikasi Penyakit Alzheimer Berdasarkan Citra MRI Otak Menggunakan ResNet50v2
Authors: Buana Dewi, Lailil Muflikhah, Budi Darma Setiawan
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2025
Topics: Deep Learning; Medical Imaging
Abstract: Penyakit Alzheimer merupakan salah satu bentuk demensia yang disebabkan oleh kerusakan atau malafungsi sel otak, sehingga menghambat kinerja otak secara optimal. Deteksi dini menjadi krusial untuk memperlambat perkembangan penyakit, namun metode diagnosis konvensional masih bergantung pada pemeriksaan klinis yang bersifat subjektif dan memerlukan waktu serta biaya tinggi. Seiring kemajuan teknologi, Convolutional Neural Networks (CNN) semakin efektif dalam analisis data medis, termasuk dalam diagnosis Alzheimer. Penelitian ini bertujuan untuk mengembangkan model klasifikasi penyakit Alzheimer berbasis Convolutional Neural Network (CNN) dengan memanfaatkan arsitektur pre-trained ResNet50V2. Dataset yang digunakan terdiri dari 6.400 citra MRI otak yang terbagi ke dalam empat kategori: Non-Demented, Very Mild Demented, Mild Demented, dan Moderate Demented. Model dikustomisasi dengan penambahan Dense Layer dan Batch Normalization, serta dioptimasi menggunakan Adam optimizer dan fungsi loss categorical cross-entropy. Penelitian ini mengevaluasi model ResNet50V2 untuk klasifikasi citra MRI otak berdasarkan metrik akurasi, precision, recall, dan F1-score, serta menganalisis pengaruh fine-tuning terhadap peningkatan akurasi model. Hasil penelitian menunjukkan bahwa model mencapai akurasi 92.7%, dengan precision, recall, dan F1-score yang konsisten. Teknik fine-tuning terbukti berhasil meningkatkan akurasi dari 73% menjadi 92, 7% serta menurunkan test loss dari 0, 59 menjadi 0,198. Peningkatan ini dicapai melalui penyesuaian arsitektur model serta optimasi …
Prediksi Nefropati Menggunakan Algoritma Random Forest
Authors: Muhammad Rusydi Hanan, Lailil Muflikhah, Fitra Abdurrachman Bachtiar
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2025
Topics: Machine Learning; Random Forest
Abstract: Prediksi nefropati diabetik yang efektif dan efisien penting untuk mencegah komplikasi serius, seperti gagal ginjal. Model machine learning dapat memberikan prediksi cepat dan akurat. Data pasien nefropati diproses melalui prapemrosesan untuk mengoptimalkan model, termasuk konversi data kategorikal ke numerik, normalisasi menggunakan Min-Max, dan oversampling dengan SMOTE untuk menyeimbangkan distribusi kelas. Fitur diseleksi berdasarkan importance score untuk mengurangi kompleksitas model. Random Forest digunakan dengan parameter optimal yang diperoleh dari Hyperparameter Tuning. Hasil menunjukkan bahwa model Random Forest dengan data prapemrosesan memiliki performa lebih baik dibandingkan model tanpa prapemrosesan, meski perbedaannya tidak terlihat signifikan. Model ini juga lebih unggul dibandingkan model machine learning lainnya seperti KNN, SVM, dan Gaussian Naïve Bayes dalam akurasi, presisi, reecall, f1 score, dan AUC score (0.96), yang menunjukkan kemampuannya dalam membedakan data positif dan negatif. Penelitian ini berhasil mengimplementasikan model Random Forest dalam memprediksi penyakit nefropati diabetik dengan hasil yang cukup baik, namun penelitian yang lebih mendalam dapat dilakukan untuk mengoptimalkan model dalam memprediksi nefropati diabetik.