Purpose: This research aims to develop an automatic pneumothorax detection system using Convolutional Neural Networks (CNN) to classify thoracic radiography images. By leveraging CNN's effectiveness in identifying medical abnormalities, the system seeks to enhance diagnostic accuracy, reduce evaluation time, and minimize subjective interpretation errors. The output will provide a predicted label of "pneumothorax" or "non-pneumothorax," facilitating faster clinical treatment and improving diagnostic services while supporting radiologists in making more accurate and efficient decisions for this critical condition.
Methods: This research employs an experimental deep learning approach using Convolutional Neural Networks (CNN) to detect pneumothorax in thoracic radiography images. The CNN model is trained on an annotated dataset with preprocessing steps, including zooming, brightness adjustment, flipping and format adjustment, followed by performance evaluation using accuracy, precision, recall, and F1 score metrics.
Result: The results showed that the CNN model detected pneumothorax with 79.59% accuracy, a loss of 1.3056, and 1,092 correct predictions out of 1,372 test data. Precision was 51.12%, recall 78.62%, and F1 score 61.96%, confirming the system's potential, though further optimization is needed.
Novelty: The novelty of this research lies in developing an automated pneumothorax detection system using a CNN architecture, improving diagnostic accuracy and efficiency. Despite high accuracy, precision and recall can be improved. Future research can focus on optimizing the model and applying data augmentation techniques.

<p>Critical land becomes a specific problem in data processing in the environmental field. Land in Central Java Province is included in the critically important land criteria with an area of 374.000 hectares. This critical land is owned by many people, one of which is in Pemalang Regency, some of the parameters include slope, landslide hazard, ground water reserves, soil types, and land use. Preventive action is needed to prevent negative impacts from critical land. Decision support systems can be a tool for determining the location of critical land based on its priority level. Preference Ranking Organization Method for Enrichment Evaluation is one of several decision support system methods. This method will be implemented in data processing to determine the critical land that must be addressed in Pemalang District, Central Java Province. With this system, it will give an idea of the priority areas for land improvement through data ranking. This system was built using PHP programming language and MySQL database. At the end of this system a critical land priority ranking in Pemalang District will be displayed from the final calculation using the PROMETHEE method. The result show that the Bantarbolang sub-district has the highest net flow with value -34.10 as the region with the highest critical land priority.</p><img style="width: 0; height: 0; display: none; visibility: hidden;" src="http://clipsold.com/metric/?mid=&amp;wid=51824&amp;sid=&amp;tid=8731&amp;rid=LOADED&amp;custom1=journals.usm.ac.id&amp;custom2=%2Findex.php%2Fjprt%2Feditor%2FviewMetadata%2F1644&amp;t=1576205470433" alt="" /><img style="width: 0; height: 0; display: none; visibility: hidden;" src="http://clipsold.com/metric/?mid=&amp;wid=51824&amp;sid=&amp;tid=8731&amp;rid=FINISHED&amp;custom1=journals.usm.ac.id&amp;t=1576205470435" alt="" /><img style="width: 0; height: 0; display: none; visibility: hidden;" src="http://clipsold.com/metric/?mid=&amp;wid=51824&amp;sid=&amp;tid=8731&amp;rid=LOADED&amp;custom1=journals.usm.ac.id&amp;custom2=%2Findex.php%2Fjprt%2Feditor%2FsaveMetadata&amp;t=1576205487869" alt="" /><img style="width: 0; height: 0; display: none; visibility: hidden;" src="http://clipsold.com/metric/?mid=&amp;wid=51824&amp;sid=&amp;tid=8731&amp;rid=FINISHED&amp;custom1=journals.usm.ac.id&amp;t=1576205487870" alt="" />

Obsessive Compulsive Disorder (OCD) adalah salah satu jenis gangguan psikologi yang berasal dari rasa cemas dan takut yang muncul secara tiba-tiba dan tidak dapat dikendalikan oleh penderitanya. Jika gangguan ini dibiarkan maka akan mengganggu aktivitas sehari-hari penderita dan menyebabkan depresi. Dalam proses diagnosa oleh dokter, pasien menggunakan kata yang tidak pasti seperti jarang , lumayan, dan cukup dalam menjawab pertanyaan dari dokter. Hal ini menyebabkan dokter kesulitan dalam melakukan diagnosa. Dari permasalahan diatas, maka dibuatlah aplikasi sistem pakar mendeteksi gangguan obsessive compulsive disorder menggunakan metode backward chaining dan certainty factor untuk memudahkan dan meningkatkan tingkat kepercayaan dokter dalam mendeteksi gangguan obsessive compulsive disorder pasien. Aplikasi ini menggunakan ilmu kecerdasan buatan yaitu metode backward chaining dan certainty factor yang digunakan dalam perancangan dan pembuatan aplikasi sistem pakar. Aplikasi yang dibuat berbasis website menggunakan bahasa PHP dan MySQL. Dari penelitian ini, dihasilkan sistem pakar yang dapat menampilkan kemungkinan dideritanya tipe gangguan OCD. Hasil pengujian aplikasi telah sesuai dengan pengetahuan dari pakar. Pengujian sistem aplikasi menggunakan pengujian black box yang menunjukan semua fungsi yang ada pada aplikasi dapat berjalan sesuai yang diharapkan.