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"In the modern era, our lifestyles are very fast-moving; this makes us highly susceptible to diseases, especially those associated with heart problems. In this research, we developed a portable early detection system for cardiac disorders. This system consists of passive electrodes, named SHIELD-EKG-EMG-PA; a shield which allows Arduino-like boards to capture electrocardiography (ECG) and electromyography (EMG) signals, named SHIELD-EKG-EMG, both devices produced by Olimex; a microcontroller, based on Arduino Uno; and an expert system which is implemented by a personal computer. This system detects time intervals of various segments in ECG signals which are captured by the devices; it then analyzes the signals in order to determine whether the patient has cardiac disorders. We call our detecting system the HEALTHCOR system. A database was established, containing various possible values of parameters in ECG signals. The types of diseases that can be detected are heart rhythm disorders including sinus bradycardia, sinus tachycardia, sinus arrhythmia, and cardiac symptoms associated with intervals and the wave height, such as myocardial infarction. From our tests, the accuracy of our system is 96%. The resultant diagnoses of four patients are all appropriate, and used a commercial 12-lead electrocardiograph."

"Secara global, termasuk di Indonesia, ibu hamil merupakan populasi rentan untuk mengalami gejala kecemasan dan depresi. Apabila kedua gejala tersebut tidak teridentifikasi dan ditata laksana selama kehamilan akan menimbulkan dampak terhadap kesehatan ibu seperti, depresi pascapersalinan, pre eklampsi, bahkan bunuh diri, dan kesehatan anak seperti, prematur dan berat badan lahir rendah. Oleh karena itu, skrining gejala kecemasan dan depresi pada ibu hamil sangat penting dilakukan. Indonesia telah memiliki kebijakan penilaian kesehatan jiwa dalam antenatal care (ANC) sejak Juli 2021. Indonesia juga sedang melakukan transformasi kesehatan digital untuk meningkatkan mutu pelayanan kesehatan. Namun, Indonesia belum memiliki instrumen dan protokol skrining gejala kecemasan dan depresi dalam pelayanan antenatal baik secara konvensional maupun pemanfaatan teknologi digital. Tujuan penelitian ini adalah mengembangkan sistem skrining gejala kecemasan dan depresi pada ibu hamil berbasis expert system dalam ANC. Penelitian ini terdiri dari tiga tahap. Tahap ke-1 menguji validitas dan reliabilitas instrumen Edinburgh Postnatal Depression Scale (EPDS) versi bahasa Indonesia pada 125 ibu hamil secara daring melalui penyebaran formulir Google. Tahap ke-2 menilai kemampuan skrining instrumen EPDS dibandingkan dengan instrumen MINI-International Neuropsychiatric Interview (MINI) sebagai gold standard pada 298 ibu hamil. Tahap ke-2 ini dilakukan di Kota Depok (Puskesmas Beji, Cipayung, Jati Jajar, dan Pancoran Mas). Penilaian MINI dilakukan secara daring oleh dua orang enumerator terlatih. Tahap ke-3 dilakukan pengembangan prototipe sistem skrining gejala kecemasan dan depresi berbasis expert system. Analisis statistik pada tahap 1 dengan berbagai jenis uji validitas dan reliabilitas. Tahap ke-2 dilakukan penilaian sensitivitas dan spesifisitas. Tahap ke-3 dilakukan evaluasi terhadap akurasi expert system dan fisibilitas prototipe. Penelitian ini menghasilkan instrumen EPDS versi bahasa Indonesia yang terbukti valid dan reliabel untuk digunakan pada populasi ibu hamil. Instrumen ini memiliki sensitivitas dan spesifisitas > 90% untuk skrining gejala kecemasan dan depresi kehamilan. Proporsi akurasi pada expert system > 90%. Ibu hamil menyatakan prototipe ini mudah, waktu penilaian singkat, dan bermanfaat untuk digunakan. Prototipe berbasis expert system yang disebut BMoms, layak dan mampu laksana untuk skrining gejala kecemasan dan depresi pada ibu hamil dalam ANC. Oleh karena itu, prototipe BMoms dapat dikembangkan lebih lanjut menjadi aplikasi siap dan tepat guna sehingga menjadi solusi inovatif untuk skrining kesehatan jiwa pada ibu hamil.

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Globally, including Indonesia, pregnant women are vulnerable population to experience symptoms of anxiety and depression. If these two symptoms are not identified and treated during pregnancy, it will have an impact on maternal and child health, such as suicide, pre-eclampsia, postpartum depression, premature, and low birth weight. Therefore, screening for symptoms of anxiety and depression in pregnant women is very important. However, Indonesia does not yet have instruments and protocols for screening symptoms of anxiety and depression in pregnant women in antenatal care (ANC) both conventionally and the use of digital technology. In fact, Indonesia already has a mental health assessment policy in ANC since July, 2021 and is currently carrying out a digital health transformation to improve the quality of health services. This study aimed to develop a screening system for symptoms of anxiety and depression in pregnant women based on an expert system in ANC.

The study consists of three stages. Phase 1 tested the validity and reliability of the Indonesian version of the Edinburgh Postnatal Depression Scale (EPDS) instrument on 125 pregnant women online through the dissemination of Google forms. Phase 2 assessed the screening ability of EPDS instrument compared to MINI-International Neuropsychiatric Interview (MINI) instrument as gold standard in 298 pregnant women. This 2nd phase was carried out in Depok City (Beji, Cipayung, Jati Jajar, and Pancoran Mas public health centre). The MINI assessment was carried out online by two trained enumerators. The 3rd stage was carried out by developing a prototype of an anxiety and depression symptom screening system based on expert system. Statistical analysis at stage 1 employed various types of validity and reliability tests. The 2nd stage was carried out to test sensitivity and specificity. The 3rd stage was evaluated on the accuracy of the expert system and the feasibility of the prototype. This study produced an Indonesian version of the EPDS instrument that was proven to be valid and reliable for use in the population of pregnant women. This instrument had a sensitivity and specificity of > 90% for screening for symptoms of pregnancy anxiety and depression. The proportion of accuracy in expert systems was > 90%. Pregnant women state that this prototype was easy, short assessment time, and useful to use. The prototype based on expert system called BMoms, was feasible and able to be carried out for screening symptoms of anxiety and depression in pregnant women in ANC. The BMoms prototype can be further developed into a ready and appropriate application so that it becomes an innovative solution for mental health screening in pregnant women."

"Seiring berkembangnya zaman yang menuntut kebutuhan produk berskala kecil, dibutuhkan pengembangan dalam teknologi pada skala mikro. Tool ultrasonic vibration assisted microforming adalah pengembangan alat proses manufaktur untuk dimensi berada pada sub milimeter yang memanfaatkan getaran untuk meningkatkan kualitas hasil produk. Alat ini dibuat sebagai langkah kontribusi penelitian di ranah teknologi microforming. Skripsi ini membahas pengembangan tool serta sistem pengukuran gaya pembentukan untuk proses ultrasonic vibration assisted micro punching dan stamping. Dilakukan estimasi gaya dan analisis mode kegagalan pada tool dalam kondisi statis serta simulasi proses ultrasonic vibration assisted micro punching dan stamping. Simulasi menunjukkan bahwa terdapat pengurangan pembebanan sebesar 24,3 – 39,4% pada proses micro punching dan 62,5 – 67% pada proses micro stamping dengan getaran ultrasonik. Validasi tool yang dikembangkan dilakukan dengan uji proses pembentukan produk yang ditentukan dengan menggunakan getaran ultrasonik. Hasil dari skripsi ini adalah tool dan sistem pengukuran gaya pembebanan untuk proses ultrasonic vibration assisted micro punching dan stamping.

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Along with the development of an era that demands the need for small-scale products, developments in technology on a micro-scale are needed. Tool ultrasonic vibration assisted microforming is a tool developed for manufacturing process with sub-millimeter dimension that utilizes vibration to increase product quality. The tool was created as a step to contribute in microforming technology research. This thesis discusses the tool development and measurement system for forming force for ultrasonic vibration assisted micropunching and microstamping process. Force estimation and failure mode analysis was conducted at static condition of the tool and ultrasonic vibration assisted micropunching process was simulated. The simulation shows that there was a reduction in forming force from 24,3 – 39,4% on micro punching process and 62,5 – 67% on micro stamping process. Validation of the tool developed is carried out by testing the process on the specified product. The result of this thesis is a tool and a measurement system for forming force for ultrasonic vibration assisted micropunching and microstamping."

"Cacat pada bearing biasanya ditentukan dengan mengamati karakteristik getaran. Metode ini sayangnya tidak dapat mendeteksi kecacatan visual pada permukaan dalam dan luar cincin bearing. Sebuah pengenalan pola diimplementasikan dalam paper ini untuk memecahkan masalah tersebut. Sebuah arsitektur jaringan saraf backpropagation digunakan untuk mengenali pola kecacatan visual. Arsitektur yang diusulkan ini terintegrasi dalam sebuah alir pengolahan citra digital. Tingkat pengenalan bearing yang baik adalah 92.93%, sedangkan untuk bantalan yang cacat adalah 75%. Angka ini menunjukkan integrasi jaringan syaraf tiruan dengan pengolahan citra digital dapat diterapkan untuk mendeteksi kecacatan visual pada bearing.

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Defects on a bearing is usually determined by observing its vibration characteristics. This method unfortunately can not detect the visual defects on the inner and outer ring bearing surface. A pattern recognition is implemented in this paper to solve the problem. A backpropagation neural network architecture is used to recognize the visual defect pattern. This architecture is integrated in a digital image processing chain. Recognition rate of good bearing is obtained at 92.93 %, meanwhile for defected bearing is obtained at 75 % respectively. This rate shows integrated artificial neural network with digital image processing can be implemented to detect the presence of visual bearing defect."