Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions

Diabetic ketoacidosis is a complication of diabetes that occurs when body cannot produce insulin necessarily to convert glucose into energy, instead fat is used as energy source and produce ketone as a byproduct. Ketones can be detected in urine compounds, especially when there is a large number of ketones that produce a distinctive smell of acetone. Odor sensors assembled into Electrical Nose (E-nose) system is used as self-diagnostics pre-test for diabetic’s analysis. However, diabetic’s analysis often required a subject to fast before sample testing. Currently, different prediction model for diabetic ketoacidosis are used depending on fasting or non-fasting conditions. This is inconvenience for diabetic’s analysis to be done at any time anywhere. This project aim to propose an adaptive prediction model capable to diagnose diabetic ketoacidosis in unrestricted food intake conditions. The adaptive Neuro-fuzzy Inference System (ANFIS) is proposed to build the diabetic ketoacidosis classifier. The fuzzy inference model will be used to capture both fasting and non-fasting membership functions before feeding the results for classification to the neural network model. Two sets of experimental data involving 20 diabetic patients and 20 healthy subjects were collected from CITO laboratory Semarang Central Java, Indonesia. Ethics consents were informed and agreed by the subjects before starting the data collection. This project follows the experimental methodology in verifying the hypothesis drawn. The experimental paradigm was designed to simulate fasting and non-fasting conditions. Samples data were recorded in the morning before food intake and two hours after food intake using four MQ 2, MQ 5, MQ 6 and MQ 8 sensors, in previously built Electronic Nose prototype system. A 5-fold cross-validation testing was implemented for classification results analysis. The results are highly promising with at least 90% accuracy in all testing. The proposed model has achieved 96% average accuracy in unrestricted food intake conditions. The prediction results on non-fasting and fasting data samples were recorded as 98% and 96% of average accuracy respectively. This has proven that the proposed ANFIS model is good to detect diabetic’s cases through ketoacidosis regardless of food intake. It has better performance in normal food intake as compare to fasting condition, since insulin inefficiency happened in diabetics patients will resulted in obvious acetone secretion in nonfasting condition. The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. The proposed GA-ANFIS approach provides excellent classification in accuracy, precision and recall. However, the results are only a minor improvement from the non-optimized ANFIS model since the predecessor has achieved good classification accuracy. In conclusion, diabetic ketoacidosis in unrestricted food intake conditions can be predicted using the proposed ANFIS and GA-ANFIS model. Future work should be focusing on data collection of the E-Nose sensors and the improvement of the learning algorithm robustness towards environmental noise during data acquisition, such as evaporation and contamination of odor samples

IMPLEMENTATION OF SUPPORT VECTOR MACHINE METHOD IN CLASSIFYING SCHOOL LIBRARY BOOKS WITH COMBINATION OF TF-IDF AND WORD2VEC

The development of technology in education is integral to enhancing its quality, such as implementing information technology in school libraries. Searching for books in school libraries is time-consuming due to conventional book classification, lacking organization based on classifications. Therefore, implementing information technology in school libraries is crucial to improve library management effectiveness. An innovative solution optimizing library management involves leveraging artificial intelligence, particularly machine learning. In applying machine learning to library book classification, Support Vector Machine acts as an algorithm understanding patterns and characteristics of book titles, categorizing them into Dewey Decimal Classification (DDC). The dataset comprises 10 classes aligned with DDC. Random data collection follows an 80:20 scale for training and testing data. Data preprocessing is an initial research stage, addressing imbalanced data through oversampling. Testing the SVM algorithm with a linear kernel and C = 1 parameter is conducted three times using different feature extraction methods: TF-IDF alone, Word2Vec alone, and a combination of TF-IDF and Word2Vec. Model performance evaluation employs K-Fold Cross-Validation. After the three objective tests, the most accurate book classification results were obtained using a combination of TF-IDF and Word2Vec feature extraction. It's concluded that SVM's book classification method can be applied, yielding the highest accuracy of 73% with the TF-IDF and Word2Vec feature extraction combination. This outperforms other feature extraction methods, with precision at 83%, recall at 72%, and an F1-Score of 76%

Analisis Perbandingan Kinerja Metode Rekursif dan Metode Iteratif dalam Algoritma Linear Search

Salah satu algoritma pencarian data yang paling populer adalah algoritma linear search.  Dalam proses pencarian data sebuah list menggunakan algoritma linear search dapat diterapkan dengan cara iteratif dan rekursif. Pandangan umum mengenai algoritma linear search adalah bahwa performa metode iteratif memiliki hasil yang sama dengan rekursif. Namun di beberapa penelitian menentang pernyataan tersebut yang mungkin tidak berlaku pada semua kasus. Dari analisis tersebut, penelitian ini berfokus pada perbandingan metode rekursif dan iteratif pada algoritma linear search untuk mengetahui algoritma mana yang paling sesuai, efisien dan efektif. Penelitian dilakukan menggunakan 3 studi kasus dengan masing-masing data sebanyak 1 juta, 10 juta, dan 100 juta. Penelitian berfokus pada hasil penggunaan memori dan waktu akses pada proses pencarian data menggunakan notasi Big-O dan bahasa pemrograman Python. Hasil penelitian menunjukkan bahwa algoritma linear search secara iteratif lebih efektif dan efisien dari pada rekursif. Meskipun kedua metode tersebut memiliki kompleksitas Big-O yang sama, namun hasil dari eksekusi program menunjukkan hasil yang berbeda. Dengan hasil algoritma linear search secara iteratif memiliki hasil waktu eksekusi dan penggunaan memori yang lebih unggul yaitu waktu akses dan penggunaan memori yang lebih sedikit dibanding metode rekursif.The linear search algorithm is one of the most popular data search algorithms. In the process of searching data for a list using a linear search algorithm, it can be applied in an iterative and recursive way. The general view of linear search algorithms is that the iterative methods perform the same as recursive methods. However, some studies contradict this statement which may not apply in all cases. From this analysis, this study focuses on the comparison of recursive and iterative methods on linear search algorithms to find out which algorithm is the most suitable, efficient, and effective. The research was conducted using 3 case studies with data of 1 million, 10 million, and 100 million respectively. The research focuses on the results of memory usage and access time in the data search process using Big-O notation and Python programming language. The results show that the iterative linear search algorithm is more effective and efficient than recursive. Although both methods have the same Big-O complexity, the results of program execution show different results. With the results of an iterative linear search algorithm, the results of the execution time and memory usage are superior, namely, the access time and memory usage are less than the recursive method

SISTEM PENJAGA AKREDITASI UNGGUL (SIGAUL) UNTUK MENDUKUNG PERENCANAAN STRATEGIS AKREDITASI FAKULTAS ILMU KOMPUTER

The Faculty of Computer Science (FIK) has the largest number of active students at Dian Nuswantoro University, namely 7104 students and it is recorded that 707 students have completed MBKM in the even semester of the 2022/2023 academic year. This figure is not something that is easy to handle manually, so there needs to be an integrated system to manage MBKM at the faculty level. The system that was built was named SIGAUL or Superior Accreditation Guard System. Apart from helping with digital archiving, it will also help the faculty to measure the adequacy of accreditation. The method used in this research is Extreme Programming which is adaptive and responsive to changes in system requirements. This method is also simpler so it can support the achievement of targeted outcomes. The hope is that the system built will facilitate the performance of the FIK Student Affairs Coordinator, the LPM team, as well as the Heads of Study Programs in accommodating students who wish to take part in MBKM activities, as well as making it easier for faculties to measure the supporting points for accreditation so that they can achieve the goal of FIK still being able to maintain Excellent accreditation well.Fakultas Ilmu Komputer (FIK) memiliki jumlah mahasiswa aktif terbesar di Universitas Dian Nuswantoro yaitu sebesar 7104 mahasiswa dan tercatat 707 mahasiswa telah selesai mengikuti MBKM di semester genap tahun ajaran 2022/2023. Angka ini bukanlah hal yang mudah untuk ditangani secara manual, maka perlu adanya sistem yang terintegrasi untuk mengelola MBKM di tingkat fakultas. Sistem yang dibangun diberi nama SIGAUL atau Sistem Penjaga Akreditasi Unggul. Selain akan membantu pengarsipan digital juga akan dapat membantu pihak fakultas untuk mengukur nilai kecukupan akreditasi. Metode yang digunakan dalam penelitian ini adalah metode pengembangan sistem Extreme Programming yang memiliki memiliki 4 tahap yaitu planning (analisis kebutuhan sistem), design (perancangan usecase dan user interface), pengkodingan (VueJS dan Laravel) serta pengujian sistem. Metode ini lebih sederhana sehingga dapat mendukung tercapainya luaran yang ditargetkan. Adapun luaran yang dicapai dalam penelitian ini adalah sistem bernama SIGAUL, yang akan mempermudah kinerja Koordinator Kemahasiswaan FIK, tim LPM, juga para Kepala Program Studi dalam mewadahi mahasiswa yang berkeinginan untuk mengambil kegiatan MBKM, serta memudahkan fakultas dalam mengukur poin-poin penunjang akreditasi sehingga dapat mencapai tujuan yaitu FIK tetap dapat menjaga akreditasi Unggul dengan baik

IMPEMENTASI EKSTRAKSI BAG OF VISUAL WORD UNTUK PROSES KLASIFIKASI FINGERPRINT MENGGGUNKAN ALGORITMA KLASIFIKASI K – NEAREST NEIGHBOUR

Teknologi pengolahan citra memungkinkan manusia untuk membuat suatu sistem yang dapat mengenali suatu citra digital secara otomatis. Sistem, pengenalan sidik jari harus mampu mengindentifikasi sidik jari seseorang dari sekumpulan besar data sidik jari. Proses klasifikasi dapat mengurangi ukuran dari ruang pencarian yaitu dengan membatasi pencarian berdasarkan kelas. Metode Bag of Visual Word merupakan sebuah teknik pengolahan citra untuk menghasilkan sebuah histogram visual word yang sering digunakan untuk mempresentasikan klasifikasi data citra. Tugas akhir ini bertujuan untuk membuat sistem klasifikasi sidik jari berdasarkan kelas pola karakteristik pola sidik jari leftloop, rightloop, twinloop, dan whorl menggunakan ektraksi SURF 64 dimensi. Berdasarkan hasil eksperimen, kinerja metode Bag of Visual Word menunjukan performa yang baik dengan pengujian perubahan koefisien nilai K 1, 3, 5, 7, 9 menghasilkan akurasi untuk masing masing nilai K berturut turut 100%, 91,5%, 75%, 71%, 64,5%

Rancangan Pengembangan Aplikasi Laporan Arsip dan Surat Berbasis Website Menggunakan Metode Prioritas Moscow

Semakin banyaknya berkas-berkas penting yang merupakan berkas dari Surat Permohonan yangdiajukan ke Bidang Keberatan, Banding dan Pengurangan membuat pegawai sering kalimengalami masalah, seperti penempatan arsip berkas Surat Permohonan, dan menjagakeamanan arsip berkas Surat Permohonan, begitu juga dengan pengawasan oleh Kepala Bidangdan Kepala Seksi kepada pegawainya. Berdasarkan masalah yang ada maka dalam penelitian inidilakukan perancangan dan pembangunan sistem manajemen surat dan arsip agar lebih mudahdalam mengakses dan mengelola arsip dari berkas Surat Permohonan yang di ajukan ke BidangKeberatan, Banding dan Pengurangan. Dalam pengembangan sistem Laporan Arsip dan Surat,metode Rapid Application Development (RAD) dan metode Prioritas Moscow diterapkan dalampengembangan sistemnya. Perancangan dan pengembangan Aplikasi Laporan Arsip dan Suratpada Bidang KBP dengan menggunakan kombinasi antara Metode Moscow dan Metode RapidApplication Development (RAD) dapat diterapkan dengan baik di sistem yang memiliki banyakuser level. dengan proses RAD yang dibagi menjadi beberapa modul, teknik Moscow denganmenentukan prioritas Kebutuhan sistem dapat diterapkan. Kedepannya bisa menambahkan fituruntuk pembuatan berkas yang dikerjakan oleh Penelaah Keberatan, supaya semua berkasnyabisa dikelola dengan baik di Aplikasi Laporan Arsip dan Surat

Implementasi Algoritma Floyd Warshall Pada Aplikasi Dewan Masjid Indonesia (Dmi) Kota Semarang Untuk Menentukan Masjid Terdekat

Location Based Service (LBS) is a service on smartphones that functions as a navigation device based on the user's position to determine the location where the user is. LBS utilizes GPS capabilities in finding geolocation information and sometimes using Google maps to display a complete map of the location. But the results of previous research studies Google Map does not give shortest and accessible routes. Furthermore, to improve work of LBS, Floyd Warshall algorithm is used because the algorithm has the principle of optimality in calculating the total of all routes optimally. According to data recorded by the Ministry of Religion of the Republic of Indonesia there have been 1,304 Mosques in the City of Semarang, but with this much data it should be easier to find places of worship for Muslims. Most mosques that are visited are mosques on the highway because it is more visible even though there are many other mosques that can be accessed. By using the White Box and Black Box tests, finding shortest path to find places of worship in the city of Semarang can be given accurately. The result was the Floyd Warshall algorithm could provide shortest path route and it was more accessible better than Google Map navigation

Performance Comparison of Convolutional Neural Network and MobileNetV2 for Chili Diseases Classification

Chili is an important agricultural commodity in Indonesia and plays an significant role in the economic growth of the country. Its demand from households and industries reaches up to 61%. However, this high demand also means that monitoring efforts must be intensified, particularly for chili plant diseases that can greatly impact yields. If these diseases are not addressed promptly, they can lead to a decrease in production levels, which can negatively affect the economy. With technological advancements, automatic monitoring using image processing is now highly feasible, making monitoring more efficient and effective. Common chili plant diseases include chili leaf yellowing disease, chili leaf curling disease, cercospora leaf spots, and magnesium deficiency with symptoms that can be observed through the shape and color of the leaves. This research aims to classify chili plant diseases by comparing the CNN algorithm and the pre-trained MobileNetV2 based model performance using the Confussion Matrix. The study shows that the MobileNetV2 model, trained with a learning rate of 0.001, produces a more optimal model with an accuracy of 90% and based on the calculation of the confusion matrix, the average percentage values for recall, precision, and F1 score are 92%. These findings highlight the potential

Developing BacaBicara: An Indonesian Lipreading System as an Independent Communication Learning for the Deaf and Hard-of-Hearing

Deaf and hard-of-hearing people have limitations in communication, espe-cially on aspects of language, intelligence, and social adjustment. To com-municate, deaf people use sign language or lipreading. For normal people, it is very difficult to use sign language. They have to memorize many hand signs. Therefore, lipreading is a necessary for communication between nor-mal and deaf people. In Indonesia, there is still few education media for deaf people to learn lipreading. To overcome this challenge, we develop a lipread-ing educational media to help deaf and hard-of-hearing to learn Bahasa In-donesia, called BacaBicara. User-Centered Design (UCD) is implemented to design the application and to analyze the constraints and conceptual models for the needs of users. This conceptual model uses the picture, lipreading video, text, and sign language to help the users understand the contents. The High fidelity prototype was implemented for evaluating usability testing. Based on the evaluation of the application, the results show that the proto-type matches the usability goals and the user experience