Financial management efficiency of islamic boarding school based on information technology

Utilization of information technology is a very important requirement for every component of society. Easy, fast and accurate data and information access can optimize routine daily work. Mawaridussalam Islamic boarding school Batang Kuis as an Islamic education institution that plays a role in shaping the character and personality of the next generation of the nation is expected to be able to optimally manage finances and provide good service to the academics of the Islamic boarding school. Until now, financial management is still done manually using bookkeeping, there is no specific financial application used in Islamic boarding school financial management, so it is difficult and slow in recording, processing, controlling and reporting financial activities. Based on these problems, an application is needed that can be used by the treasurer and board of boarding school leaders for financial management systematically. The method applied is the User Centered Design approach and the target that was successfully achieved is the availability of web-based financial management applications that are used properly to increase the efficiency and effectiveness of financial management in Islamic boarding schools

Sistem Pendeteksian Manusia untuk Keamanan Ruangan menggunakan Viola – Jones

Aspek keamanan sangat dibutuhkan dalam berbagai kehidupan saat ini seperti keamanan rumah, gedung, atau ruangan yang memiliki nilai penting bagi pemilik. Keamanan dapat dikerjakan oleh tenaga manusia tetapi cara ini kurang efisien karena menghabiskan banyak resources seperti uang, waktu, tenaga dan juga sangat rentan terhadap kelalaian manusia (human error). Oleh karena itu diperlukan suatu pendetekatan untuk dapat melakukan keamanan tersebut.Salah satu pendekatan yang dapat dilakukan adalah dengan melakukan pendeteksian objek manusia melalui kamera yang terhubung dengan komputer.Dalam penelitian ini digunakan Viola-Jones untuk mendeteksi objek manusia dalam citra berdasarkan fitur. Citra yang diinput dari webcam dengan fungsi capture dalam library OpenCV diubah menjadi citra abu-abu setelah mengalami proses scaling, dilanjutkan ekualisasi histogram, perhitungan fitur dengan citra integral, dan pendeteksian objek dengan cascade of classifier. Pada penelitian ini ditunjukkan bahwa metode yang diajukan mampu melakukan pendeteksian objek dengan hasil akurasi mencapai 86,88% . Kata Kunci : viola-jones, pendeteksian manusia, keamanan ruangan, cascade of classifier, opencv

Harnessing the hybrid power supply systems of utility grid and photovoltaic panels at retrofit residential single family building in Medan

The paper describes improvisation mode of energy supply source by collaboration between national utility grid as represented by fossil fuels and PV as independent renewable power resource in order to aim the energy consumptions efficiently in retrofit single family house. In this case, one existing single family house model in Medan, Indonesia was observed for the possibility of future refurbishment. The eco-design version of the house model and prediction analyses regarding nearby potential renewable energy resource (solar system) had been made using Autodesk Revit MEP 2015, Climate Consultant 6.0 and Green Building Studio Analysis. Economical evaluation of using hybrid power supply is discussed as well. © Published under licence by IOP Publishing Ltd

Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network

In this paper, we propose an approach to detect and geotag advertisement billboard in real-time condition. Our approach is using AlexNet’s Deep Convolutional Neural Network (DCNN) as a pre-trained neural network with 1000 categories for image classification. To improve the performance of the pre-trained neural network, we retrain the network by adding more advertisement billboard images using inductive transfer learning approach. Then, we fine-tuned the output layer into advertisement billboard related categories. Furthermore, the detected advertisement billboard images will be geotagged by inserting Exif metadata into the image file. Experimental results show that the approach achieves 92.7% training accuracy for advertisement billboard detection, while for overall testing results it will give 71,86% testing accuracy

VISUALISASI SUARA JANTUNG MANUSIA PADA PLATFORM MOBILE

ABSTRAKPerkembangan ilmu pengetahuan dan teknologi di bidang kesehatan semakin memberikan kemudahan dalam mediagnosa penyakit jantung. Perekaman suara jantung merupakan salah satu contoh perkembangan tersebut. Hasil auskultasi dari stetoskop tidak bisa dilihat secara visual dan tidak pernah tersimpan di dalam stetoskop, sehingga tidak bisa didengar bersama dokter lain sebagai bahan diskusi. Maka diperlukan suatu implementasi yang dapat menampilkan karakteristik  suara jantung agar dapat terlihat secara visual . Seiring dengan perkembangan komputasi mobile, maka dibuat aplikasi untuk visualisasi suara jantung pada platform android, yang diharapkan mampu dijadikan sebagai bahan diskusi dan pembelajaran bagi penggunanya. Visualisasi yang dibuat merupakan tampilan grafik dari sinyal suara jantung normal dan abnormal, serta menampilkan informasi durasi S1, S2, systole, dan diastole. Hasil visualisasi yang cukup maksimal didapatkan dengan menggunakan file dengan tipe .wav. Berdasarkan pengujian terhadap delapan jenis data suara jantung yang digunakan, maka diperoleh hasil bahwa durasi diastole lebih lama dibandingkan dengan systole, dan durasi suara jantung pertama lebih lama dibandingkan dengan suara jantung kedua.Kata Kunci; Auskultasi, Suara Jantung, Visualisasi, Android. PENDAHULUANPada bidang kedokteran stetoskop memiliki peranan penting. Stetoskop merupakan salah satu cara yang efektif untuk menilai penyakit kardiovaskular. Stetoskop menggunakan teknik auskultasi, yaitu teknik yang digunakan untuk mendiagnosa penyakit jantung melalui suara jantung. Hasil dari suara yang didengar akan digunakan ahli medis sebagai dasar dalam mendiagnosa penyakit jantung.Auskultasi suara jantung dengan stetoskop memiliki beberapa kendala, selain karena frekuensi dan amplitudo suara jantung yang rendah, faktor noise dan penilaian yang subjektif dari dokter juga sangat mempengaruhi. Selain itu teknik auskultasi juga membutuhkan pengalaman dan keterampilan dalam memahami karakteristik suara yang dihasilkan stetoskop. Suara yang dihasilkan stetoskop tidak pernah tersimpan sehingga tidak bisa didengar bersama dokter lain sebagai bahan diskusi. Selain itu suara jantung yang sama dapat diinterpretasikan berbeda oleh dokter yang berbeda.Untuk mengatasi kendala dari cara kerja stetoskop, maka dibutuhkan alat bantu lain agar para dokter lebih mudah, cepat dan akurat dalam melakukan proses klasifikasi suara jantung, yaitu menggunakan proses komputasi. Salah satu perkembangan teknologi pada bidang kardiologi adalah Phonocardiogram.Dengan adanya hasil PCG dari pasien, ahli medis dapat mendengar kembali, menganilisis dan mengolah data tersebut sesuai dengan kebutuhan

The Role of Faster R-CNN Algorithm in the Internet of Things to Detect Mask Wearing: The Endemic Preparations

Faster R-CNN is an algorithm development that continuously starts from CNN then R-CNN and Faster R-CNN. The development of the algorithm is needed to test whether the heuristic algorithm has optimal provisions. Broadly speaking, faster R-CNN is included in algorithms that are able to solve neural network and machine learning problems to detect a moving object. One of the moving objects in the current phenomenon is the use of masks. Where various countries in the world have issued endemic orations after the Covid 19 pandemic occurred. Detection tool has been prepared that has been tested at the mandatory mask door, namely for mask users. In this paper, the role of the Faster R-CNN algorithm has been carried out to detect masks poured on Internet of Thinks (IoT) devices to automatically open doors for standard mask users. From the results received that testing on the detection of moving mask objects when used reaches 100% optimal at a distance of 0.5 to 1 meter and 95% at a distance of 1.5 to 2 meters so that the process of sending detection signals to IoT devices can be carried out at a distance of 1 meter at the position mask users to automatic door

Applications For Detecting The Rate Of Fruit In Mangrove Plants

Mangrove plants are one of the plants that really help aquatic ecosystems between the sea, coast, and land. Mangrove plants provide many ecological, social, and economic benefits. In Indonesia, mangrove plants have 202 species with the same anatomy as other plants in general, consisting of roots, fruits, stems and leaves. Nowadays, the location of mangrove plants in Indonesia has experienced the fastest damage in the world due to conversion to ponds, settlements, industry and plantations. One of the efforts to restore aesthetic value and restore the ecological function of mangrove forest areas is rehabilitation using mangrove fruit. In the rehabilitation process, farmers generally use the manual method with the naked eye to determine fruit ripeness on mangrove plants, so the resulting level of accuracy is not optimal. To overcome this problem, an application is needed that can facilitate farmers in determining fruit maturity in mangrove plants so that it can help determine the maturity level of mangrove fruit. The development of this application utilizes the Deep Learning method as well as the utilization of digital image processing techniques with Grayscaling, Adaptive Threshold, Sharpening and Smoothing techniques. The results of this study are an application that can detect the level of fruit maturity in mangrove plants with an accuracy of 99.11%. With this application, determining the maturity level of fruit on mangrove plants can be easily done

Observing the Performance of the TextRank Algorithm on Automatic Text Summarization for Bahasa Indonesia

The research about automatic text summarization is common in English text. According to the previous study, automatic text summarization in Bahasa Indonesia is still challenging due to research in this area, especially the research which discusses TextRank algorithm performance, which is still meagerly. Accordingly, this research observes the performance of the TextRank algorithm to summarize the text in Bahasa Indonesia. The TextRank algorithm summarizes a text by sorting out the essential words and relevant sentences regardless of the source language. This algorithm uses a vertex to represent a word. The similarity measurement process will calculate the overlapping words (the same word between two vertices). These overlapping words are represented by the edge, which connects the vertices. Thus, the text forms a graph. This research focuses on the similarity measurement process to determine relevant sentences in a text. As the similarity measurement is critical for the summarization result, this research switches the original process to the Levenshtein Distance algorithm and observes its performance. This research uses the human-produced summarized text by the expert in Bahasa Indonesia linguistics to evaluate the result. The evaluation method is conducted by using ROUGE-1 and ROUGE-2. The result shows that the average of ROUGE-1 and ROUGE-2 for the TextRank algorithm is 0.439 and 0.3186, respectively. Meanwhile, the modified TextRank obtains 0.3999 and 0.2805, respectively. Both of the algorithms have not shown satisfactory results as expected